diff --git a/CMakeLists.txt b/CMakeLists.txt
index 7ba77b3..836cc93 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,6 +1,23 @@
 cmake_minimum_required(VERSION 3.0.0)
 project(rpclib_benchmark)
 
+macro(remove_cxx_flag flag)
+    string(REPLACE "${flag}" "" CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE}")
+endmacro()
+
+option(USE_O2 "Use -O2 instead of the default -O3" OFF)
+
+if (NOT CMAKE_BUILD_TYPE STREQUAL "Release")
+    message(WARNING "Not building in release mode!")
+else()
+    if (USE_O2)
+        remove_cxx_flag("-O3")
+        set(CMAKE_CXX_FLAGS_RELEASE "-O2 ${CMAKE_CXX_FLAGS_RELEASE}")
+    endif()
+    message(STATUS "Release flags: ${CMAKE_CXX_FLAGS_RELEASE}")
+endif()
+
+
 set(CMAKE_EXPORT_COMPILE_COMMANDS "ON") # for YCM
 
 if(EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/build/conanbuildinfo.cmake) #Clion, with conanbuildinfo.cmake in build folder
@@ -26,7 +43,7 @@ add_executable(rpclib_benchmark
     src/capnp/struct_helpers.cc
     )
 
-target_link_libraries(rpclib_benchmark ${CONAN_LIBS} dw bfd)
+target_link_libraries(rpclib_benchmark ${CONAN_LIBS})
 set_target_properties(
         rpclib_benchmark
         PROPERTIES
diff --git a/README.md b/README.md
index e81a127..95964e1 100644
--- a/README.md
+++ b/README.md
@@ -1,43 +1,73 @@
 # rpclib benchmarks
 
 This is a set of benchmarks that compares the performance of various RPC libraries/frameworks in
-specific situations.
+specific situations. The benchmark suite is created as part of the [rpclib](http://rpclib.net)
+project.
 
 ## Libraries
 
-This is a WIP. The following libraries are or will be benchmarked:
+The following libraries are or will be benchmarked:
 
   - [x] [rpclib](http://rpclib.net)
   - [x] [cap'n'proto rpc](https://capnproto.org/cxxrpc.html)
   - [x] [Apache Thrift](https://thrift.apache.org/)
   - [x] [grpc](https://grpc.io)
-  - [x] Direct function calls (for comparison)
+  - [x] +1: Direct function calls (for comparison)
 
 ## Results
 
-TBD
+The results can be found in this repository (json files and plots, plus a [Jupyter Notebook](https://github.com/rpclib/benchmarks/blob/master/rpclib%20benchmark%20results.ipynb)). I've also written a blog post about this: http://szelei.me/rpc-benchmark-part1/
 
 ## Compiling and running
 
-TBD
+Currently, this benchmark set can only be compiled on Linux. Even though all libraries are cross-platform, the conan packages I'm using aren't.
+
+### Requirements
+
+  * A reasonably modern Linux distribution (tested on Arch Linux and Ubuntu 16.04)
+  * g++ 5.4 or later, or clang-3.8 or later
+  * cmake 3.x
+  
+#### Build steps
+
+```
+mkdir build && cd build
+conan install .. --build missing
+cmake ..
+make
+```
+
+#### Running the benchmarks
+
+I suggest using the `run_benchmarks.sh` script. It took about 40 minutes no my machine to complete all benchmark runs.
 
 ## FAQ
 
 ### What kind of benchmarks are performed?
 
-This is a WIP. These are the benchmarks that are implemented or will be implemented for each
+These are the benchmarks that are implemented or will be implemented for each
 library from the above list.
 
   - [x] `get_answer`: Perform a function call that returns a number and deserialize
     the result (so that it's available for the user code as an `int`)
   - [x] `get_blob`: Perform a function call that returns a large blob and deserialize it into
-    a byte buffer.
-  - [ ] `get_composite`: Perform a function call that returns a nested struct with lots of
-    fields and lots of data in it.
+    a byte buffer. This is performed in increasing sizes.
+  - [x] `get_struct`: Perform a function call that returns a nested struct with lots of
+    fields and lots of data in it and intentionally bad alignment.
+
+Each library is set up to create local client and server that communicate through a TCP socket.
+
+### How are the benchmarks ran?
+
+This benchmark suite uses [Google Benchmark](https://github.com/google/benchmark) to manage the
+benchmarking. Each library has a fixture that is set up and torn down in each benchmark run once
+(similarly to unit tests). The setup and teardown performance is not measured. Each microbenchmark is performed in a number of
+iterations (determined by Google Benchmark). All runs are performed five times. The results are the means, medians and standard deviations of the
+remaining three measured times.
 
 ### What is `rpclib`?
 
-`rpclib` is a modern msgpack-rpc implementation for library. It provides a very clean interface
+`rpclib` is a modern msgpack-rpc implementation for C++. It provides a very clean interface
 inspired by Boost.Python. See [rpclib.net](http://rpclib.net) for more information.
 
 ### I think your implementation for library XYZ is suboptimal. Will you change it?
@@ -52,9 +82,8 @@ One or more of the following reasons:
   * I did not know about the library
   * There was no conan package for it and I didn't have time to make one
   * I didn't have time to learn the library
+  * The library wasn't to my knowledge widespread enough to be interesting for this comparison.
 
-So by all means, please let me know if there is library that is worth looking at. Bonus points if
+However, by all means, please let me know if there is one that is worth looking at. Bonus points if
 you make a conan package and/or pull request with benchmark code for it.
 
-
-
diff --git a/benchmark.json b/benchmark.json
new file mode 100644
index 0000000..9296d0e
--- /dev/null
+++ b/benchmark.json
@@ -0,0 +1,3441 @@
+{
+  "context": {
+    "date": "2017-02-20 20:42:49",
+    "num_cpus": 4,
+    "mhz_per_cpu": 2807,
+    "cpu_scaling_enabled": true,
+    "library_build_type": "release"
+  },
+  "benchmarks": [
+    {
+      "name": "direct_bench/get_blob/1024",
+      "iterations": 44665986,
+      "real_time": 94,
+      "cpu_time": 94,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/1024",
+      "iterations": 44665986,
+      "real_time": 94,
+      "cpu_time": 94,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/1024",
+      "iterations": 44665986,
+      "real_time": 94,
+      "cpu_time": 94,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/1024",
+      "iterations": 44665986,
+      "real_time": 94,
+      "cpu_time": 94,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/1024",
+      "iterations": 44665986,
+      "real_time": 94,
+      "cpu_time": 94,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/1024_mean",
+      "iterations": 44665986,
+      "real_time": 94,
+      "cpu_time": 94,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/1024_stddev",
+      "iterations": 0,
+      "real_time": 0,
+      "cpu_time": 0,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/2k",
+      "iterations": 38137145,
+      "real_time": 110,
+      "cpu_time": 110,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/2k",
+      "iterations": 38137145,
+      "real_time": 110,
+      "cpu_time": 110,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/2k",
+      "iterations": 38137145,
+      "real_time": 110,
+      "cpu_time": 110,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/2k",
+      "iterations": 38137145,
+      "real_time": 110,
+      "cpu_time": 110,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/2k",
+      "iterations": 38137145,
+      "real_time": 110,
+      "cpu_time": 110,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/2k_mean",
+      "iterations": 38137145,
+      "real_time": 110,
+      "cpu_time": 110,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/2k_stddev",
+      "iterations": 0,
+      "real_time": 0,
+      "cpu_time": 0,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/4k",
+      "iterations": 34357347,
+      "real_time": 122,
+      "cpu_time": 122,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/4k",
+      "iterations": 34357347,
+      "real_time": 122,
+      "cpu_time": 122,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/4k",
+      "iterations": 34357347,
+      "real_time": 122,
+      "cpu_time": 122,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/4k",
+      "iterations": 34357347,
+      "real_time": 122,
+      "cpu_time": 122,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/4k",
+      "iterations": 34357347,
+      "real_time": 122,
+      "cpu_time": 122,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/4k_mean",
+      "iterations": 34357347,
+      "real_time": 122,
+      "cpu_time": 122,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/4k_stddev",
+      "iterations": 0,
+      "real_time": 0,
+      "cpu_time": 0,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/8k",
+      "iterations": 17669037,
+      "real_time": 239,
+      "cpu_time": 239,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/8k",
+      "iterations": 17669037,
+      "real_time": 238,
+      "cpu_time": 238,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/8k",
+      "iterations": 17669037,
+      "real_time": 238,
+      "cpu_time": 238,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/8k",
+      "iterations": 17669037,
+      "real_time": 238,
+      "cpu_time": 238,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/8k",
+      "iterations": 17669037,
+      "real_time": 238,
+      "cpu_time": 238,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/8k_mean",
+      "iterations": 17669037,
+      "real_time": 238,
+      "cpu_time": 238,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/8k_stddev",
+      "iterations": 0,
+      "real_time": 0,
+      "cpu_time": 0,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/16k",
+      "iterations": 8342779,
+      "real_time": 501,
+      "cpu_time": 501,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/16k",
+      "iterations": 8342779,
+      "real_time": 501,
+      "cpu_time": 501,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/16k",
+      "iterations": 8342779,
+      "real_time": 502,
+      "cpu_time": 502,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/16k",
+      "iterations": 8342779,
+      "real_time": 501,
+      "cpu_time": 501,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/16k",
+      "iterations": 8342779,
+      "real_time": 501,
+      "cpu_time": 501,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/16k_mean",
+      "iterations": 8342779,
+      "real_time": 501,
+      "cpu_time": 501,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/16k_stddev",
+      "iterations": 0,
+      "real_time": 0,
+      "cpu_time": 0,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/32k",
+      "iterations": 3746935,
+      "real_time": 1112,
+      "cpu_time": 1112,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/32k",
+      "iterations": 3746935,
+      "real_time": 1112,
+      "cpu_time": 1112,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/32k",
+      "iterations": 3746935,
+      "real_time": 1112,
+      "cpu_time": 1112,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/32k",
+      "iterations": 3746935,
+      "real_time": 1112,
+      "cpu_time": 1112,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/32k",
+      "iterations": 3746935,
+      "real_time": 1117,
+      "cpu_time": 1116,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/32k_mean",
+      "iterations": 3746935,
+      "real_time": 1113,
+      "cpu_time": 1113,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/32k_stddev",
+      "iterations": 0,
+      "real_time": 2,
+      "cpu_time": 2,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/64k",
+      "iterations": 1977641,
+      "real_time": 2126,
+      "cpu_time": 2126,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/64k",
+      "iterations": 1977641,
+      "real_time": 2125,
+      "cpu_time": 2125,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/64k",
+      "iterations": 1977641,
+      "real_time": 2125,
+      "cpu_time": 2125,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/64k",
+      "iterations": 1977641,
+      "real_time": 2125,
+      "cpu_time": 2125,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/64k",
+      "iterations": 1977641,
+      "real_time": 2127,
+      "cpu_time": 2127,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/64k_mean",
+      "iterations": 1977641,
+      "real_time": 2126,
+      "cpu_time": 2126,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/64k_stddev",
+      "iterations": 0,
+      "real_time": 1,
+      "cpu_time": 1,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/128k",
+      "iterations": 967483,
+      "real_time": 4335,
+      "cpu_time": 4335,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/128k",
+      "iterations": 967483,
+      "real_time": 4335,
+      "cpu_time": 4335,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/128k",
+      "iterations": 967483,
+      "real_time": 4334,
+      "cpu_time": 4334,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/128k",
+      "iterations": 967483,
+      "real_time": 4337,
+      "cpu_time": 4337,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/128k",
+      "iterations": 967483,
+      "real_time": 4337,
+      "cpu_time": 4337,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/128k_mean",
+      "iterations": 967483,
+      "real_time": 4335,
+      "cpu_time": 4335,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/128k_stddev",
+      "iterations": 0,
+      "real_time": 1,
+      "cpu_time": 1,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/256k",
+      "iterations": 431289,
+      "real_time": 9769,
+      "cpu_time": 9769,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/256k",
+      "iterations": 431289,
+      "real_time": 9755,
+      "cpu_time": 9755,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/256k",
+      "iterations": 431289,
+      "real_time": 9778,
+      "cpu_time": 9778,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/256k",
+      "iterations": 431289,
+      "real_time": 9801,
+      "cpu_time": 9801,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/256k",
+      "iterations": 431289,
+      "real_time": 9796,
+      "cpu_time": 9796,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/256k_mean",
+      "iterations": 431289,
+      "real_time": 9780,
+      "cpu_time": 9780,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/256k_stddev",
+      "iterations": 0,
+      "real_time": 17,
+      "cpu_time": 17,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/512k",
+      "iterations": 197855,
+      "real_time": 21253,
+      "cpu_time": 21253,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/512k",
+      "iterations": 197855,
+      "real_time": 21235,
+      "cpu_time": 21235,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/512k",
+      "iterations": 197855,
+      "real_time": 21242,
+      "cpu_time": 21242,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/512k",
+      "iterations": 197855,
+      "real_time": 21223,
+      "cpu_time": 21223,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/512k",
+      "iterations": 197855,
+      "real_time": 21244,
+      "cpu_time": 21244,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/512k_mean",
+      "iterations": 197855,
+      "real_time": 21239,
+      "cpu_time": 21239,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/512k_stddev",
+      "iterations": 0,
+      "real_time": 10,
+      "cpu_time": 10,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/1024k",
+      "iterations": 91148,
+      "real_time": 46019,
+      "cpu_time": 46019,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/1024k",
+      "iterations": 91148,
+      "real_time": 46004,
+      "cpu_time": 46004,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/1024k",
+      "iterations": 91148,
+      "real_time": 46000,
+      "cpu_time": 46000,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/1024k",
+      "iterations": 91148,
+      "real_time": 46039,
+      "cpu_time": 46039,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/1024k",
+      "iterations": 91148,
+      "real_time": 45998,
+      "cpu_time": 45998,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/1024k_mean",
+      "iterations": 91148,
+      "real_time": 46012,
+      "cpu_time": 46012,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/1024k_stddev",
+      "iterations": 0,
+      "real_time": 15,
+      "cpu_time": 15,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/2M",
+      "iterations": 23026,
+      "real_time": 183074,
+      "cpu_time": 183073,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/2M",
+      "iterations": 23026,
+      "real_time": 185560,
+      "cpu_time": 185559,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/2M",
+      "iterations": 23026,
+      "real_time": 183469,
+      "cpu_time": 183468,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/2M",
+      "iterations": 23026,
+      "real_time": 183129,
+      "cpu_time": 183128,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_blob/2M",
+      "iterations": 23026,
+      "real_time": 182812,
+      "cpu_time": 182810,
+      "time_unit": "ns"
+    },
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+      "name": "thrift_bench/get_blob/2M",
+      "iterations": 2493,
+      "real_time": 2015536,
+      "cpu_time": 1670433,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_blob/2M",
+      "iterations": 2493,
+      "real_time": 2013253,
+      "cpu_time": 1672365,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_blob/2M",
+      "iterations": 2493,
+      "real_time": 2031855,
+      "cpu_time": 1684620,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_blob/2M_mean",
+      "iterations": 2493,
+      "real_time": 2015654,
+      "cpu_time": 1673536,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_blob/2M_stddev",
+      "iterations": 0,
+      "real_time": 8606,
+      "cpu_time": 5625,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_blob/4M",
+      "iterations": 1198,
+      "real_time": 3986573,
+      "cpu_time": 3375523,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_blob/4M",
+      "iterations": 1198,
+      "real_time": 3968072,
+      "cpu_time": 3359253,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_blob/4M",
+      "iterations": 1198,
+      "real_time": 3974296,
+      "cpu_time": 3369077,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_blob/4M",
+      "iterations": 1198,
+      "real_time": 3963688,
+      "cpu_time": 3364244,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_blob/4M",
+      "iterations": 1198,
+      "real_time": 3978982,
+      "cpu_time": 3377046,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_blob/4M_mean",
+      "iterations": 1198,
+      "real_time": 3974322,
+      "cpu_time": 3369029,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_blob/4M_stddev",
+      "iterations": 0,
+      "real_time": 8049,
+      "cpu_time": 6707,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_blob/8M",
+      "iterations": 628,
+      "real_time": 7534389,
+      "cpu_time": 6555491,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_blob/8M",
+      "iterations": 628,
+      "real_time": 7520133,
+      "cpu_time": 6541102,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_blob/8M",
+      "iterations": 628,
+      "real_time": 7472858,
+      "cpu_time": 6495355,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_blob/8M",
+      "iterations": 628,
+      "real_time": 7524242,
+      "cpu_time": 6545810,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_blob/8M",
+      "iterations": 628,
+      "real_time": 7535442,
+      "cpu_time": 6561960,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_blob/8M_mean",
+      "iterations": 628,
+      "real_time": 7517413,
+      "cpu_time": 6539944,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_blob/8M_stddev",
+      "iterations": 0,
+      "real_time": 23032,
+      "cpu_time": 23454,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_blob/16M",
+      "iterations": 317,
+      "real_time": 15217941,
+      "cpu_time": 13434821,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_blob/16M",
+      "iterations": 317,
+      "real_time": 14948557,
+      "cpu_time": 13157772,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_blob/16M",
+      "iterations": 317,
+      "real_time": 15244783,
+      "cpu_time": 13458538,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_blob/16M",
+      "iterations": 317,
+      "real_time": 15214563,
+      "cpu_time": 13426219,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_blob/16M",
+      "iterations": 317,
+      "real_time": 15008874,
+      "cpu_time": 13218637,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_blob/16M_mean",
+      "iterations": 317,
+      "real_time": 15126944,
+      "cpu_time": 13339197,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_blob/16M_stddev",
+      "iterations": 0,
+      "real_time": 122968,
+      "cpu_time": 125227,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_structs",
+      "iterations": 1077,
+      "real_time": 3875821,
+      "cpu_time": 3875650,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_structs",
+      "iterations": 1077,
+      "real_time": 3872932,
+      "cpu_time": 3872660,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_structs",
+      "iterations": 1077,
+      "real_time": 3844185,
+      "cpu_time": 3843778,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_structs",
+      "iterations": 1077,
+      "real_time": 3848681,
+      "cpu_time": 3848363,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_structs",
+      "iterations": 1077,
+      "real_time": 3853672,
+      "cpu_time": 3853206,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_structs_mean",
+      "iterations": 1077,
+      "real_time": 3859058,
+      "cpu_time": 3858731,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_structs_stddev",
+      "iterations": 0,
+      "real_time": 12895,
+      "cpu_time": 12976,
+      "time_unit": "ns"
+    },
+    {
+      "name": "rpclib_bench/get_structs",
+      "iterations": 876,
+      "real_time": 8439091,
+      "cpu_time": 4864700,
+      "time_unit": "ns"
+    },
+    {
+      "name": "rpclib_bench/get_structs",
+      "iterations": 876,
+      "real_time": 8395446,
+      "cpu_time": 4836706,
+      "time_unit": "ns"
+    },
+    {
+      "name": "rpclib_bench/get_structs",
+      "iterations": 876,
+      "real_time": 8421884,
+      "cpu_time": 4839861,
+      "time_unit": "ns"
+    },
+    {
+      "name": "rpclib_bench/get_structs",
+      "iterations": 876,
+      "real_time": 8391460,
+      "cpu_time": 4835050,
+      "time_unit": "ns"
+    },
+    {
+      "name": "rpclib_bench/get_structs",
+      "iterations": 876,
+      "real_time": 7881467,
+      "cpu_time": 4384601,
+      "time_unit": "ns"
+    },
+    {
+      "name": "rpclib_bench/get_structs_mean",
+      "iterations": 876,
+      "real_time": 8305870,
+      "cpu_time": 4752184,
+      "time_unit": "ns"
+    },
+    {
+      "name": "rpclib_bench/get_structs_stddev",
+      "iterations": 0,
+      "real_time": 212920,
+      "cpu_time": 184106,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_structs",
+      "iterations": 496,
+      "real_time": 11759067,
+      "cpu_time": 8416373,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_structs",
+      "iterations": 496,
+      "real_time": 11245855,
+      "cpu_time": 7912415,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_structs",
+      "iterations": 496,
+      "real_time": 11868540,
+      "cpu_time": 9065434,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_structs",
+      "iterations": 496,
+      "real_time": 11629326,
+      "cpu_time": 8325662,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_structs",
+      "iterations": 496,
+      "real_time": 11737504,
+      "cpu_time": 8935075,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_structs_mean",
+      "iterations": 496,
+      "real_time": 11648059,
+      "cpu_time": 8530992,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_structs_stddev",
+      "iterations": 0,
+      "real_time": 214967,
+      "cpu_time": 421152,
+      "time_unit": "ns"
+    },
+    {
+      "name": "grpc_bench/get_structs",
+      "iterations": 382,
+      "real_time": 17501989,
+      "cpu_time": 11003504,
+      "time_unit": "ns"
+    },
+    {
+      "name": "grpc_bench/get_structs",
+      "iterations": 382,
+      "real_time": 17570151,
+      "cpu_time": 11061160,
+      "time_unit": "ns"
+    },
+    {
+      "name": "grpc_bench/get_structs",
+      "iterations": 382,
+      "real_time": 17752578,
+      "cpu_time": 11103080,
+      "time_unit": "ns"
+    },
+    {
+      "name": "grpc_bench/get_structs",
+      "iterations": 382,
+      "real_time": 17774696,
+      "cpu_time": 11046295,
+      "time_unit": "ns"
+    },
+    {
+      "name": "grpc_bench/get_structs",
+      "iterations": 382,
+      "real_time": 17781095,
+      "cpu_time": 11161818,
+      "time_unit": "ns"
+    },
+    {
+      "name": "grpc_bench/get_structs_mean",
+      "iterations": 382,
+      "real_time": 17676102,
+      "cpu_time": 11075171,
+      "time_unit": "ns"
+    },
+    {
+      "name": "grpc_bench/get_structs_stddev",
+      "iterations": 0,
+      "real_time": 116734,
+      "cpu_time": 53764,
+      "time_unit": "ns"
+    },
+    {
+      "name": "capnp_bench/get_structs",
+      "iterations": 22833,
+      "real_time": 184136,
+      "cpu_time": 184129,
+      "time_unit": "ns"
+    },
+    {
+      "name": "capnp_bench/get_structs",
+      "iterations": 22833,
+      "real_time": 182602,
+      "cpu_time": 182595,
+      "time_unit": "ns"
+    },
+    {
+      "name": "capnp_bench/get_structs",
+      "iterations": 22833,
+      "real_time": 183575,
+      "cpu_time": 183570,
+      "time_unit": "ns"
+    },
+    {
+      "name": "capnp_bench/get_structs",
+      "iterations": 22833,
+      "real_time": 183861,
+      "cpu_time": 183855,
+      "time_unit": "ns"
+    },
+    {
+      "name": "capnp_bench/get_structs",
+      "iterations": 22833,
+      "real_time": 182901,
+      "cpu_time": 182896,
+      "time_unit": "ns"
+    },
+    {
+      "name": "capnp_bench/get_structs_mean",
+      "iterations": 22833,
+      "real_time": 183415,
+      "cpu_time": 183409,
+      "time_unit": "ns"
+    },
+    {
+      "name": "capnp_bench/get_structs_stddev",
+      "iterations": 0,
+      "real_time": 578,
+      "cpu_time": 578,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_answer",
+      "iterations": 1000000000,
+      "real_time": 2,
+      "cpu_time": 2,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_answer",
+      "iterations": 1000000000,
+      "real_time": 2,
+      "cpu_time": 2,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_answer",
+      "iterations": 1000000000,
+      "real_time": 2,
+      "cpu_time": 2,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_answer",
+      "iterations": 1000000000,
+      "real_time": 2,
+      "cpu_time": 2,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_answer",
+      "iterations": 1000000000,
+      "real_time": 2,
+      "cpu_time": 2,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_answer_mean",
+      "iterations": 1000000000,
+      "real_time": 2,
+      "cpu_time": 2,
+      "time_unit": "ns"
+    },
+    {
+      "name": "direct_bench/get_answer_stddev",
+      "iterations": 0,
+      "real_time": 0,
+      "cpu_time": 0,
+      "time_unit": "ns"
+    },
+    {
+      "name": "rpclib_bench/get_answer",
+      "iterations": 885606,
+      "real_time": 22259,
+      "cpu_time": 4800,
+      "time_unit": "ns"
+    },
+    {
+      "name": "rpclib_bench/get_answer",
+      "iterations": 885606,
+      "real_time": 22294,
+      "cpu_time": 4738,
+      "time_unit": "ns"
+    },
+    {
+      "name": "rpclib_bench/get_answer",
+      "iterations": 885606,
+      "real_time": 21822,
+      "cpu_time": 4637,
+      "time_unit": "ns"
+    },
+    {
+      "name": "rpclib_bench/get_answer",
+      "iterations": 885606,
+      "real_time": 22173,
+      "cpu_time": 4793,
+      "time_unit": "ns"
+    },
+    {
+      "name": "rpclib_bench/get_answer",
+      "iterations": 885606,
+      "real_time": 22028,
+      "cpu_time": 4659,
+      "time_unit": "ns"
+    },
+    {
+      "name": "rpclib_bench/get_answer_mean",
+      "iterations": 885606,
+      "real_time": 22115,
+      "cpu_time": 4725,
+      "time_unit": "ns"
+    },
+    {
+      "name": "rpclib_bench/get_answer_stddev",
+      "iterations": 0,
+      "real_time": 173,
+      "cpu_time": 67,
+      "time_unit": "ns"
+    },
+    {
+      "name": "capnp_bench/get_answer",
+      "iterations": 115159,
+      "real_time": 36326,
+      "cpu_time": 36324,
+      "time_unit": "ns"
+    },
+    {
+      "name": "capnp_bench/get_answer",
+      "iterations": 115159,
+      "real_time": 36293,
+      "cpu_time": 36291,
+      "time_unit": "ns"
+    },
+    {
+      "name": "capnp_bench/get_answer",
+      "iterations": 115159,
+      "real_time": 36275,
+      "cpu_time": 36273,
+      "time_unit": "ns"
+    },
+    {
+      "name": "capnp_bench/get_answer",
+      "iterations": 115159,
+      "real_time": 36295,
+      "cpu_time": 36293,
+      "time_unit": "ns"
+    },
+    {
+      "name": "capnp_bench/get_answer",
+      "iterations": 115159,
+      "real_time": 36269,
+      "cpu_time": 36267,
+      "time_unit": "ns"
+    },
+    {
+      "name": "capnp_bench/get_answer_mean",
+      "iterations": 115159,
+      "real_time": 36292,
+      "cpu_time": 36290,
+      "time_unit": "ns"
+    },
+    {
+      "name": "capnp_bench/get_answer_stddev",
+      "iterations": 0,
+      "real_time": 20,
+      "cpu_time": 20,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_answer",
+      "iterations": 593383,
+      "real_time": 10328,
+      "cpu_time": 6580,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_answer",
+      "iterations": 593383,
+      "real_time": 10401,
+      "cpu_time": 6612,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_answer",
+      "iterations": 593383,
+      "real_time": 10135,
+      "cpu_time": 6519,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_answer",
+      "iterations": 593383,
+      "real_time": 10310,
+      "cpu_time": 6610,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_answer",
+      "iterations": 593383,
+      "real_time": 10251,
+      "cpu_time": 6579,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_answer_mean",
+      "iterations": 593383,
+      "real_time": 10285,
+      "cpu_time": 6580,
+      "time_unit": "ns"
+    },
+    {
+      "name": "thrift_bench/get_answer_stddev",
+      "iterations": 0,
+      "real_time": 89,
+      "cpu_time": 34,
+      "time_unit": "ns"
+    },
+    {
+      "name": "grpc_bench/get_answer",
+      "iterations": 126052,
+      "real_time": 53044,
+      "cpu_time": 31945,
+      "time_unit": "ns"
+    },
+    {
+      "name": "grpc_bench/get_answer",
+      "iterations": 126052,
+      "real_time": 55396,
+      "cpu_time": 32654,
+      "time_unit": "ns"
+    },
+    {
+      "name": "grpc_bench/get_answer",
+      "iterations": 126052,
+      "real_time": 55306,
+      "cpu_time": 32747,
+      "time_unit": "ns"
+    },
+    {
+      "name": "grpc_bench/get_answer",
+      "iterations": 126052,
+      "real_time": 56212,
+      "cpu_time": 33090,
+      "time_unit": "ns"
+    },
+    {
+      "name": "grpc_bench/get_answer",
+      "iterations": 126052,
+      "real_time": 54245,
+      "cpu_time": 32252,
+      "time_unit": "ns"
+    },
+    {
+      "name": "grpc_bench/get_answer_mean",
+      "iterations": 126052,
+      "real_time": 54841,
+      "cpu_time": 32538,
+      "time_unit": "ns"
+    },
+    {
+      "name": "grpc_bench/get_answer_stddev",
+      "iterations": 0,
+      "real_time": 1094,
+      "cpu_time": 399,
+      "time_unit": "ns"
+    }
+  ]
+}
diff --git a/conanfile.txt b/conanfile.txt
index 815bc4b..406ed95 100644
--- a/conanfile.txt
+++ b/conanfile.txt
@@ -2,15 +2,11 @@
 google-benchmark/1.1.0@cpace6/stable
 capnproto/0.5.3@lasote/stable
 thrift/0.9.3@sztomi/testing
-grpc/1.1.0-pre1@sztomi/testing
-rpclib/experimental@sztomi/testing
-backward/1.3.0@Manu343726/testing
+grpc/1.1.0@sztomi/testing
+rpclib/1.0.0@sztomi/testing
 
 [options]
 rpclib:enable_logging=False
-backward:stack_walking_backtrace=True
-backward:stack_details_dw=True
-backward:stack_details_bfd=True
 protobuf:shared=True
 
 [generators]
diff --git a/include/capnp/fixture.h b/include/capnp/fixture.h
index b300d22..923d929 100644
--- a/include/capnp/fixture.h
+++ b/include/capnp/fixture.h
@@ -24,7 +24,7 @@ class capnp_bench : public benchmark::Fixture {
     }
 
     kj::Promise<void> getBlob(GetBlobContext context) override {
-      auto blob = ::get_blob(blob_size_);
+      auto& blob = ::get_blob(blob_size_);
       kj::ArrayPtr<unsigned char> arr((unsigned char *)blob.data(),
                                       blob.size());
       context.getResults().setResult(capnp::Data::Reader(arr));
@@ -72,10 +72,8 @@ class capnp_bench : public benchmark::Fixture {
     blob_size_ = param;
     auto request = client->getBlobRequest();
     auto resultPromise = request.send();
-    std::string s;
-    auto result =
-        resultPromise.wait(client_thing->getWaitScope()).getResult().asChars();
-    benchmark::DoNotOptimize(s = result.begin());
+    auto result = resultPromise.wait(client_thing->getWaitScope());
+    benchmark::DoNotOptimize(result.getResult().size());
   }
 
   void get_structs(int param) {
diff --git a/include/direct/fixture.h b/include/direct/fixture.h
index 257c71a..1628618 100644
--- a/include/direct/fixture.h
+++ b/include/direct/fixture.h
@@ -18,10 +18,10 @@ class direct_bench : public benchmark::Fixture {
   }
 
   void get_blob(int param) {
-    std::string s;
-    benchmark::DoNotOptimize(s = ::get_blob(param));
+    const std::string* s;
+    benchmark::DoNotOptimize(s = &::get_blob(param));
     std::size_t size;
-    benchmark::DoNotOptimize(size = s.size());
+    benchmark::DoNotOptimize(size = s->size());
   }
 
   void get_structs(int param) {
diff --git a/include/grpc/fixture.h b/include/grpc/fixture.h
index 3dccdab..95ee27b 100644
--- a/include/grpc/fixture.h
+++ b/include/grpc/fixture.h
@@ -80,8 +80,7 @@ class grpc_bench : public benchmark::Fixture {
     grpc_code::EmptyRequest request;
     grpc_code::BlobResponse response;
     auto status = client_.get_blob(&client_context, request, &response);
-    std::string s;
-    benchmark::DoNotOptimize(s = response.data());
+    std::string const& s = response.data();
     std::size_t size;
     benchmark::DoNotOptimize(size = s.size());
   }
diff --git a/include/rpclib/fixture.h b/include/rpclib/fixture.h
index bde810a..463054a 100644
--- a/include/rpclib/fixture.h
+++ b/include/rpclib/fixture.h
@@ -30,6 +30,7 @@ class rpclib_bench : public benchmark::Fixture {
   }
 
   void TearDown(benchmark::State&) override {
+    server->stop();
     server.reset();
     client.reset();
   }
diff --git a/include/target_code.h b/include/target_code.h
index 9d94254..055d13e 100644
--- a/include/target_code.h
+++ b/include/target_code.h
@@ -11,7 +11,7 @@
 
 
 int get_answer(int num);
-std::string get_blob(int size);
+const std::string& get_blob(int size);
 
 std::string rand_str(std::size_t length);
 
diff --git a/plots/get_answer_cpu_linear.svg b/plots/get_answer_cpu_linear.svg
new file mode 100644
index 0000000..b85a772
--- /dev/null
+++ b/plots/get_answer_cpu_linear.svg
@@ -0,0 +1,1282 @@
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+     <g id="text_9">
+      <!-- 5.0 -->
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+       <use x="55.615234" xlink:href="#ArialMT-2e"/>
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+       <use xlink:href="#ArialMT-31"/>
+       <use x="55.615234" xlink:href="#ArialMT-32"/>
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+       <use x="55.615234" xlink:href="#ArialMT-35"/>
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+       <use x="55.615234" xlink:href="#ArialMT-30"/>
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diff --git a/rpclib benchmark results.ipynb b/rpclib benchmark results.ipynb
new file mode 100644
index 0000000..62abbae
--- /dev/null
+++ b/rpclib benchmark results.ipynb	
@@ -0,0 +1,372 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 365,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from pandas import *\n",
+    "from pandas.io.json import json_normalize\n",
+    "import pandas as pd\n",
+    "import json\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 366,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "raw_data = json.load(open('benchmark.json'))['benchmarks']\n",
+    "libs = ['direct', 'capnp', 'rpclib', 'thrift']\n",
+    "frames = {}\n",
+    "for lib in libs:\n",
+    "    key = '{}_bench/get_blob'.format(lib)\n",
+    "    data = [b for b in raw_data if ('mean' in b['name'] and key in b['name'])]\n",
+    "    for i, b in enumerate(data):\n",
+    "        b['name'] = b['name'].split('/')[2].split('_')[0]\n",
+    "        b['cpu_time'] = b['cpu_time'] / 1000000\n",
+    "        b['real_time'] = b['real_time'] / 1000000\n",
+    "        b['time_unit'] = 'ms'\n",
+    "    p_data = json_normalize(data)\n",
+    "    frames[lib] = p_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 367,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f507acb8ac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "all = pd.concat(frames)\n",
+    "del all['iterations']\n",
+    "del all['time_unit']\n",
+    "plt.figure(figsize=(16, 8))\n",
+    "all.reset_index(level=0, inplace=True)\n",
+    "all.rename(columns={'level_0': 'library'}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 368,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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473+ncPfdf+KZZ/7FT3/6Y37/+1uZPfu19nVnzZrFd75zOk89NZnrr7+GW2/9NXvssXfN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\n4QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f507b1e2828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 8))\n",
+    "colors = ['#F25C84', '#3DBFF2', '#ACF266', '#F9A575', '#AB58FC']\n",
+    "sns.set_palette(colors)\n",
+    "g = sns.barplot(x='name', y='cpu_time', data=all, hue=\"library\")\n",
+    "g.legend(loc=2,prop={'size':14})\n",
+    "sns.plt.title('get_blob: cpu times per iteration (smaller is better)')\n",
+    "g.set(xlabel='Blob size (bytes)', ylabel='Iteration time (ms)')\n",
+    "g.get_figure().savefig('plots/get_blob_cpu_linear.svg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 369,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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7vpcxYz7CRhu9neuv/2nTt0uSJKm/sie7BSy//AoMHDiQIUOGMmTIUACOP/4k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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f507a3a06a0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 8))\n",
+    "g = sns.barplot(x='name', y='cpu_time', data=all, hue=\"library\")\n",
+    "g.set_yscale('log')\n",
+    "g.legend(loc=2,prop={'size':14})\n",
+    "sns.plt.title('get_blob: cpu times per iteration on a logarithmic scale (smaller is better)')\n",
+    "g.set(xlabel='Blob size (bytes)', ylabel='Iteration time (ms)')\n",
+    "g.get_figure().savefig('plots/get_blob_cpu_log.svg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 370,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Xpze5ffEe2wwaP6nHNlX94mMVNTdbvdB5zSNHLjaok6YOF5EkSZKqZsiWJEmS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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f507a975dd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 8))\n",
+    "g = sns.barplot(x='name', y='real_time', data=all, hue=\"library\")\n",
+    "g.set_yscale('log')\n",
+    "g.legend(loc=2,prop={'size':14})\n",
+    "sns.plt.title('get_blob: wall clock times per iteration on a logarithmic scale (smaller is better)')\n",
+    "g.set(xlabel='Blob size (bytes)', ylabel='Iteration time (ms)')\n",
+    "g.get_figure().savefig('plots/get_blob_real_log.svg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 371,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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6n1axFsce+2X23ffTjBq1Sq+XHT/+EgYPHgTAXXfdyd13/5Hzz/8RK664Epdf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\nMzMzswZOmM3MzMzMGnwCBOo3QGFHrRsAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f507ac3ecc0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 8))\n",
+    "g = sns.barplot(x='name', y='real_time', data=all, hue=\"library\")\n",
+    "#g.set_yscale('log')\n",
+    "g.legend(loc=2,prop={'size':14})\n",
+    "sns.plt.title('get_blob: wall clock times per iteration on a logarithmic scale (smaller is better)')\n",
+    "g.set(xlabel='Blob size (bytes)', ylabel='Iteration time (ms)')\n",
+    "g.get_figure().savefig('plots/get_blob_real_linear.svg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 372,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "libs = ['capnp', 'direct', 'rpclib', 'thrift', 'grpc']\n",
+    "get_answer_data = {}\n",
+    "frames = {}\n",
+    "for lib in libs:\n",
+    "    for b in raw_data:\n",
+    "        if b['name'] == '{}_bench/get_answer_mean'.format(lib):\n",
+    "            b['cpu_time'] = b['cpu_time'] / 1000000\n",
+    "            b['real_time'] = b['real_time'] / 1000000\n",
+    "            b['time_unit'] = 'ms'\n",
+    "            get_answer_data[lib] = b\n",
+    "    p_data = json_normalize(get_answer_data[lib])\n",
+    "    frames[lib] = p_data\n",
+    "all = pd.concat(frames)\n",
+    "all.reset_index(level=0, inplace=True)\n",
+    "all.rename(columns={'level_0': 'library'}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 373,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+972P+vo15HJlfOMbX2LlypWcccZZm/x4bCrFBOqewHJgj4JpecBALUmS1In0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\nYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f507d3120b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 8))\n",
+    "g = sns.barplot(x='library', y='cpu_time', data=all, order=libs)\n",
+    "g.set(ylim=(0, 0.06))\n",
+    "sns.plt.title('get_answer: cpu times per iteration on a linear scale (smaller is better)')\n",
+    "g.set(xlabel='Library', ylabel='Iteration time (ms)')\n",
+    "for p in g.patches:\n",
+    "    g.annotate(str(p.get_height()), (p.get_x() + p.get_width() / 2, p.get_height() + 0.0005), ha='center', va='bottom')\n",
+    "g.get_figure().savefig('plots/get_answer_cpu_linear.svg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 374,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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0t/YuZl0TEQcCDxumtSrsoZYkSZIKqFgPdf5jhWvIfmywGDi29LRTETGC7FykDWQnv78v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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f507a3bb828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 8))\n",
+    "g = sns.barplot(x='library', y='real_time', data=all, order=libs)\n",
+    "g.set(ylim=(0, 0.06))\n",
+    "sns.plt.title('get_answer: wall clock times per iteration on a linear scale (smaller is better)')\n",
+    "g.set(xlabel='Library', ylabel='Iteration time (ms)')\n",
+    "for p in g.patches:\n",
+    "    g.annotate(str(p.get_height()), (p.get_x() + p.get_width() / 2, p.get_height() + 0.0005), ha='center', va='bottom')\n",
+    "g.get_figure().savefig('plots/get_answer_real_linear.svg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 375,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "get_answer_data = {}\n",
+    "frames = {}\n",
+    "for lib in libs:\n",
+    "    for b in raw_data:\n",
+    "        if b['name'] == '{}_bench/get_structs_mean'.format(lib):\n",
+    "            b['cpu_time'] = b['cpu_time'] / 1000000\n",
+    "            b['real_time'] = b['real_time'] / 1000000\n",
+    "            b['time_unit'] = 'ms'\n",
+    "            get_answer_data[lib] = b\n",
+    "    p_data = json_normalize(get_answer_data[lib])\n",
+    "    frames[lib] = p_data\n",
+    "all = pd.concat(frames)\n",
+    "all.reset_index(level=0, inplace=True)\n",
+    "all.rename(columns={'level_0': 'library'}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 376,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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S1ipFu2xeCOFy4GfAy3mTjwPGAe1I7iJ2ZIxxaQjhAWAgSY/5jcBXSO7Qtn+M8Z3mtjN7\n9nyv+ydJkqSi6tatpskhH63+OtQGakmSJBVbc4G6mFf5kCRJkj73DNSSJElSBgZqSZIkKQMDtSRJ\nkpSBgVqSJEnKwEAtSZIkZWCgliRJkjIwUEuSJEkZGKglSZKkDAzUkiRJUgYGakmSJCkDA7UkSZKU\ngYFakiRJysBALUmSJGVgoJYkSZIyMFBLkiRJGRioJUmSpAwM1JIkSVIGBmpJkiQpAwO1JEmSlIGB\nWpIkScrAQC1JkiRlYKCWJEmSMjBQS5IkSRkYqCVJkqQMDNSSJElSBgZqSZIkKQMDtSRJkpSBgVqS\nJEnKwEAtSZIkZWCgliRJkjIwUEuSJEkZGKglSZKkDAzUkiRJUgYGakmSJCkDA7UkSZKUgYFakiRJ\nysBALUmSJGVgoJYkSZIyMFBLkiRJGRioJUmSpAwM1JIkSVIGBmpJkiQpAwO1JEmSlIGBWpIkScrA\nQC1JkiRlUFHMxkMI2wLnxxh3DiHcCqyfzuoFPBlj/HnesmXAW8Cr6aSpMcZTilmfJEmSlFXRAnUI\n4TfAgcACgOXhOYTQBfgH8OsGq/QGno0x7lGsmiRJkqQ1rZhDPl4DftLI9DOAK2KMbzeY3g/oEUL4\nRwjh3hBCKGJtkiRJ0hpRtB7qGOOdIYRe+dNCCOsCu/LZ3mmAt4FzY4x3hBB2BG4Gtl7Zdrp0qaai\nonwNVCxJkiStuqKOoW7ET4HJMcaljcx7BqgDiDE+HkLoHkIoizHmmmtw7tzaIpQpSZIkfapbt5om\n55X6Kh/fA/7axLwxwK8AQgh9gVkrC9OSJElSSyt1D3UAXl9hQggPAAOB84CbQwg/JumpPqTEtUmS\nJEmrrCyXa92dwLNnz2/dOyBJkqS1XrduNWVNzfPGLpIkSVIGBmpJkiQpAwO1JEmSlIGBWpIkScrA\nQC1JkiRlYKCWJEmSMjBQS5IkSRkYqCVJkqQMDNSSJElSBgZqSZIkKQMDtSRJkpSBgVqSJEnKwEAt\nSZIkZWCgliRJkjIwUEuSJEkZGKglSZKkDAzUkiRJUgYGakmSJCkDA7UkSZKUgYFakiRJysBALUmS\nJGVgoJYkSZIyMFBLkiRJGRioJUmSpAwM1JIkSVIGBmpJkiQpAwO1JEmSlIGBWpIkScrAQC1JkiRl\nYKCWJEmSMjBQS5IkSRkYqCVJkqQMDNSSJElSBgZqSZIkKQMDtSRJkpSBgVqSJEnKwEAtSZIkZWCg\nliRJkjIwUEuSJEkZGKglSZKkDAzUkiRJUgYGakmSJCkDA7UkSZKUgYFakiRJysBALUmSJGVgoJYk\nSZIyqChm4yGEbYHzY4w7hxC+BfwFeDWdPSHGeFvesu2Bm4F1gfnAwTHG2cWsT5IkScqqaIE6hPAb\n4EBgQTqpH3BJjPHiJlYZArwQYzw9hPBzYBRwXLHqkyRJktaEYg75eA34Sd7zfsCPQwiPhhCuDSHU\nNFh+R+C+9PFfge8VsTZJkiRpjShaoI4x3gksyZv0FHBSjHEn4HVgTINVOgEfpY/nA52LVZskSZK0\nphR1DHUDd8UYP1z+GLiiwfx5wPJe6xrgQwrQpUs1FRXla6ZCSZIkaRWVMlDfH0I4Nsb4FLAr8K8G\n86cAu5P0ZP8IeKyQRufOrV2jRUqSJEkNdevWcLTyp0oZqIcAV4QQlgDvAEcBhBAeAAYCE4AbQwiP\nA4uB/UtYmyRJkrRaynK5XEvXkMns2fNb9w5IkiRprdetW01ZU/O8sYskSZKUgYFakiRJysBALUmS\nJGVgoJYkSZIyMFBLkiRJGRioJUmSpAwM1JIkSVIGBmpJkiQpAwO1JEmSlIGBWpIkScrAQC1JkiRl\nYKCWJEmSMjBQS5IkSRkYqCVJkqQMDNSSJElSBgZqSZIkKQMDtSRJkpSBgVqSJEnKwEAtSZIkZWCg\nliRJkjIwUEuSJEkZGKglSZKkDAzUkiRJUgYGakmSJCkDA7UkSZKUgYFakiRJysBALUmSJGVgoJYk\nSZIyMFBLkiRJGRioJUmSpAwM1JIkSVIGBmpJkiQpAwO1JEmSlIGBWpIkScrAQC1JkiRlYKCWJEmS\nMjBQS5IkSRkYqCVJkqQMDNSSJElSBgZqSZIkKQMDtSRJkpSBgVqSJEnKwEAtSZIkZWCgliRJkjIw\nUEuSJEkZGKglSZKkDAzUkiRJUgYVxWw8hLAtcH6McecQwpbAFcBS4BPgoBjjuw2WfxaYlz79T4zx\n0GLWJ0mSJGVVtEAdQvgNcCCwIJ10OXBsjPG5EMLRwAjg+Lzlq4CyGOPOxapJkiRJWtOKOeTjNeAn\nec9/HmN8Ln1cASxqsHxfoDqE8EAI4aEQQv8i1iZJkiStEWW5XK5ojYcQegG3xhj7503bHrgW2CnG\nODtveh+gP/A7YDPgr0CIMdY1t426uqW5ioryIlQvSZIk1StrakZRx1A3FEL4GXAq8OP8MJ16BZgR\nY8wBr4QQ5gBfAWY11+bcubVFqVWSJElarlu3mibnlewqHyGEA4BhwM4xxtcbWeQw4OJ02e5AJ+Dt\nUtUnSZIkrY6SDPkAdgBmAzOBD9PZj8QYx4QQbgJGAe8ANwAbAjlgRIzxiZVtY/bs+cXbAUmSJAno\n1q2mySEfRQ3UpWCgliRJUrE1F6i9sYskSZKUgYFakiRJysBALUmSJGVgoJYkSZIyMFBLkiRJGRio\nJUmSpAxWeqfEEEJHYBeS24EvA2YAf48xLipybZIkSdJar8nrUIcQqoExwE+AacCbwBJgY+BbwB+B\ns2KMH5em1MZ5HWpJkiQVW3PXoW6uh/pmYCJwSoxxWf6MEEIbYGC6zF5rokhJkiSpNWquh7osxths\n728hyxSbPdSSJEkqtky3Hg8h9Ab6A5OB35IM9zg+xvjYmixydRmoJUmSVGxZbz1+PbAYGARsDhwP\nXLhmSpMkSZJat0ICdVWM8Q6SMdO3pD3TbYtbliRJktQ6FBKol4YQ9iEJ1H8JIewFLC1uWZIkSVLr\nUEigPgr4MXBMjPFt4OfAEUWtSpIkSWolVvqlRIAQQg3QJX9ajHFmsYpaFX4pUZIkScW2utehBiCE\ncBFwJDAHWN5QDthkjVQnSZIktWIrDdQkV/fo0dJ3RJQkSVLTXnxxOhMmjGP8+In108aNu5gNN9yI\nvfb66QrLLlu2jIsvPo8ZM16lbdu2nHzyaHr23IBhw46qX2bmzDf50Y8GMmTIsRx22C+pru4AQPfu\nPRg5cgwAS5cuZcyYUxg4cC/699+eJ598gptvvqG+jWnTnuOmm26jV6+Nm62ntSskUE8DKgEDtSRJ\n0lrolltu5P7776Wqqj0Ac+fOZezYMcya9Sb773/gZ5Z/7LGHWbx4Mb/97fVMn/4C48dfynnnXVIf\nxv/737c47bRTOPjgw/nkk0/I5XIrBPXly4wdexrvvfceAwcmN87u3397+vffHoDJk2+iT5++9Oq1\n8Urrae0K+VLiJGBGCOHREMJDy/8VuzBJkiQVpkePnpx99qe3CVm4sJbDDjuKH/xg90aXnzbtObbd\ndjsAttiiDy+//NIK88eNu5ghQ46lurqaGTNeZdGiRfz618cwfPhgpk9/AYDa2lpGjBjNt7+91Wfa\nf++9d7n//ns59NAjC6qntSukh/pS4DjgzSLXIkmSpNWw88678vbb/6t/3r17D7p378GTT05pdPkF\nCxbQoUPH+udt2rShrq6OiooKZsx4lQULFrDVVtsAUFVVxS9+cSB77LEXs2bN5MQThzN58p1sttnm\nTdZz2223sN9++9OuXbuC6mntCgnUH8UYbyp6JZIkSSqJDh06UFtbW/88l8tRUZHEwgceuJc999y7\nft4GG2xIz549KSsrY8MNN6Jz587MmfM+6623fqNtL1u2jClTHueoo4YWdyfWIoUE6sdDCHcCfyW5\nBTkAhmxJkqTWqU+fvkyZ8hi77vp9pk9/gU022bR+3jPPPM0vf3lw/fN77rmb116bwYknnsz7789m\nwYIFdO26TpNtv/76a2y00UZUVlYVdR/WJoUE6g7APGCHvGk5wEAtSZLUipx11mkceeRQdtppF55+\n+p8MHnwYuVyu/qodAB98MIfOnb9U/3zgwEGcffbpDBlyOGVlZZxyymn1vdmNmTnzTbp371nU/Vjb\nNHljlxBCVYxxUXMrF7JMsXljF0mSJBXb6t7Y5ZYQwn3ArTHG+fkz0jsnHgR8D9i7sZUlSZKkL4Lm\nAvW+wBDg6RDCh8BbQB3QC+gKXJ4uI0mS9IX3yFm1K19Ia4UBo6vXaHtNBuoY4zLgSuDKEEJfYDNg\nGfBajPH5NVqFJEmS1EoV8qVE0gBtiJYkSZIaKOROiZIkSZKaYKCWJEmSMihoyEcIYQegD3A9sG2M\n8dGiViVJkiS1EivtoQ4hHAeMBY4HOgK/DSGcWOzCJEmSpNagkCEfhwA/ABbEGOcAWwOHFbMoSZIk\nqbUoJFAvjTEuznu+CFhapHokSZKkVqWQQP1ICOEioEMIYS/gbuDB4pYlSZIktQ6FBOqTgFdJrkN9\nEHAv4BhqSZIkiQKu8hFjXBZCmAz8NW9yd2Bm0aqSJEmSWomVBup0uMeRwJx0UhmQAzYpYl2SJElS\nq1DIdagHAT1ijB8XuxhJkiSptSlkDPU0oLLYhUiSJEmtUSE91JOAGSGEF4C65RNjjN8tWlWSJElS\nK1FIoL4UOA54s8i1SJIkSa1OIYH6oxjjTUWvRJIkSWqFCgnUj4cQ7iS5bF79HRMN2ZIkSVJhgboD\nMA/YocF0A7UkSZK+8Aq5scuhq9t4CGFb4PwY484hhE2BG0iuYT0dOCbGuCxv2fbAzcC6wHzg4Bjj\n7NXdtiRJklQKTV42L4Twl/T//4QQXm/4b2UNhxB+A/wOqEonXQKMijF+h+TmMIMarDIEeCGdfxMw\natV3R5IkSSqt5nqoj0z/33k1234N+AnJZfcA+gGPpI//CuwG3JW3/I7ABXnzR6/mdiVJkqSSaTJQ\nxxjfTh9eEmPcJ39eCOFBYNfmGo4x3hlC6JU3qSzGmEsfzwc6N1ilE/BRM/Mb1aVLNRUV5YUsKkmS\nVES1LV2ACtStW80aba/JQB1CuAvoC3RvMMSjApi1Gttalve4Bviwwfx56fSm5jdq7lxPXkmSJBVu\n9uz5q7xOcyG8uVuPHwx8F7gf2CXv33bAgFWuAv4vhLBz+vhHwGMN5k8Bdm9mviRJkrTWaW7IxzyS\nXuOGXx5cXScA14QQ2gEvAX8ACCE8AAwEJgA3hhAeJ7ne9f5raLuSJElS0ZTlcrmVL7UWmz17fuve\nAUmS9LnwyFkOQ20tBoyuXuV1unWrKWtqXnNDPiRJkiStRCF3SiSE8A3gyyTXjwYgxvhosYqSJEmS\nWouVBuoQwpXAHsDrJHc5JP3/u0WsS5IkSWoVCumh3g0IMcaFxS5GkiRJam0KGUP9OnlDPSRJkiR9\nqpAe6g+Af4cQngAWLZ8YYzysaFVJkiRJrUQhgfq+9J8kSZKkBlY65CPGeCPwL5LbgXcBnk+nSZIk\nSV94Kw3UIYQDgT8BGwMbAX8MITjcQ5IkSaKwIR8nANvEGOcAhBDOBh4GritiXZIkSVKrUMhVPsqX\nh2mAGOP7wLLilSRJkiS1HoX0UD8fQrgMuDZ9fjjwfPFKkiRJklqPQnqojwQWkwzxuAFYAgwtYk2S\nJElSq7HSHur0Dom/KUEtkiRJUqvTZKAOITwbY/x2CGEZkMubVQbkYozlRa9OkiRJWss1GahjjN9O\n///MsJAQQmUxi5IkSZJai0KuQz21wfM2wDNFq0iSJElqRZob8vEQsHP6OP8yeXXA3cUtS5IkSWod\nmhvy8V2AEMLlMcbjSleSJEmS1HoUch3qESGEvYGOJF9ILAc2jjGeVtTKJElSk+rq6hg7dgzvvPM2\nbdq0YcSIUWy0Ua/6+bfddgt//vOf+NKXvgTAb34zkm7d1uOMM05l/vz5VFS0ZdSo0+nWbV2mT3+B\nyy+/iIqKcrbeuj+HHXYUixcv5pxzzuB///svHTp04PjjR7DBBhvy1FNPMmHCOKqq2rPttttxyCFH\nUFdXx7nnnsHbb7/NkiWLOfjgw9lxxwEtdGSk0iskUN8JVAObAo8BOwFTm11DkiQV1dSpj7N06VKu\nvvo6nn68d868AAAf5ElEQVT6SSZOvJKzz76wfn6MLzNq1Bl89atfq592++2TCeFrHHrokdx775+5\n5Zab+NWvTuSii87l7LMvoHv3Hpx00nG88srLvPDC87RvX83EiTcwc+YbXHrpBVx00TjOO+8srrji\nt/To0ZMzzxzN888/x1tvzaRTpy8xevRZzJv3EYccsr+BWl8ohQTqAGwGXE5yc5cTgT8UsyhJktS8\nDTbYiKVLl7Js2TIWLFhARcWKf9JjfImbb76eOXPmsP32O3LggYey3377s3TpUgDeffcdampqWLDg\nY5YsWUyPHj0B2Gab7Xjmmaf43//+R//+2wOw4Ya9eOON//DRRx9SU9Opftk+ffoybdpz7LPPfuyy\ny64A5HI5yssLiRfS50chd0p8N8aYA14Gvhlj/B/gZfMkSWpB7du35513/sf++/+U888/m5/+9Ocr\nzN9119048cSRjBt3NdOmPceUKY8BUF5ezvDhg7nzztvYaaedWbBgAdXVHerXq66u5uOPP2azzTbn\niSceI5fLMX36C7z//mw6derMJ58s4s0332Dp0qVMnTqFRYsWUl1dTXV1B2prFzBq1AiOPHJISY+F\n1NIKeQv5YgjhCmACcEsIoTvQtrhlSZKk5tx++2S22WY7Bg8exrvvvsNxxw3hxhtvpbKyklwux377\n7U/Hjh0B2G67HXn11cgOO3wHgHHjrubNN9/gpJOO4/rrb2Hhwtr6dmtra+nYsYYf/3hP3nzzPwwd\negR9+vQlhK9SXl7OqFFnctFF59K2bTs22aQ3nTsnY7TfffcdRo48ib33/im77fbD0h8QqQUV0kM9\nFLg9xvhvYAzwFWD/olYlSZKaVVPTiQ4dksDcqVNn6urqWLYsucrtggULOOign1FbW0sul+PZZ58m\nhK8yadL13HffPUDSw92mTTkdOnSkoqIt//3vW+RyOZ56aip9+36Ll1/+N/36bcOECdfy3e9+j+7d\newDw1FNTueSS8Vx88Tj++9+32Gqrbfjggzkcf/wwhgw5loEDB7XMAZFaUFkul2t2geW3IC9RPats\n9uz5ze+AJEmfQ7W1tZx77pnMmfM+S5YsYd99fwHAwoW1DBr0E+677x7+8IfbaNu2LVtttQ2HH340\nH3wwh7FjT2fx4k9YtmwZgwcP45vf3JLp019g3LiLWbZsGVtvvS1HH30MH374IWPGjGTRooV07FjD\nKaeMZp11unH33Xfxxz/eQWVlJbvt9kP22ednXHbZRTz00N/YcMON6uu7+OJxVFZWtczBaSGPnFW7\n8oW0VhgwunqV1+nWraasqXmFBOq/AucAT8UYP1nlrReZgVqSJK0NDNStx5oO1IWMod4KeAQghJAj\nuRZ1LsZYvsqVSJL0OZB7eHxLl6ACle08rKVL0BfASgN1jLFbKQqRJEmSWqOVBuoQQjuSa08H4Fjg\nV8B5McbFRa5NkiRJWusVcpWPK0luO94PqCO5Y+K1xSxKkiRJai0KCdT9YowjgSUxxlrgYOBbxS1L\nkiRJah0KCdS5dNjH8qtprJP3WJIkSfpCKyRQXw78HVg/hHAZ8AxwWVGrkiRJklqJQq7ycVMI4Rlg\nF6Ac2CPGOK3olUmSJEmtQCFX+bgzxrgP8O+8aQ/GGHctamWSJElSK9BkoA4h3AX0BbqHEF5vsM6s\nYhcmSZIktQbN9VAfDHyZZAz18LzpdcC7xSxKkiRJai2aDNQxxnnAPGBQ6cqRJEmSWpfmhnwso/HL\n45UBuRhjedGqkiRJklqJ5nqoC7mkniRJkvSFZmiWJEmSMjBQS5IkSRkYqCVJkqQMDNSSJElSBgZq\nSZIkKQMDtSRJkpRBc3dKXONCCIcAh6RPq4AtgfVjjB+m8y8HdgTmp8sMijF+VMoaJUmSpFVR0kAd\nY7wBuAEghHAlcN3yMJ3qB/wgxvh+KeuSJEmSVleLDPkIIWwFfCPGODFvWhtgM2BiCGFKCOGwlqhN\nkiRJWhUl7aHOMxI4o8G0DsAVwCVAOfCPEMIzMcZpzTXUpUs1FRXeBV2SVDrvtXQBKli3bjUl3Fpt\nCbelLNb0eVHyQB1C+BIQYoz/aDCrFrg8xlibLvcQ0BdoNlDPnevJK0mSGjd79vyVL6QvnNU5L5oL\n4S3RQ70T8GAj0zcHbgshfItkKMqOwI2lLEySJElaVS0RqAPwev2TEI4HZsQY7w4hTAKeBJYAN8UY\nX2yB+iRJkqSClTxQxxgvbPD8kgbzLvzMSpIkSdJayhu7SJIkSRkYqCVJkqQMDNSSJElSBgZqSZIk\nKQMDtSRJkpSBgVqSJEnKwEAtSZIkZWCgliRJkjIwUEuSJEkZGKglSZKkDAzUkiRJUgYGakmSJCkD\nA7UkSZKUgYFakiRJysBALUmSJGVgoJYkSZIyMFBLkiRJGRioJUmSpAwM1JIkSVIGBmpJkiQpAwO1\nJEmSlIGBWpIkScrAQC1JkiRlYKCWJEmSMjBQS5IkSRkYqCVJkqQMDNSSJElSBgZqSZIkKQMDtSRJ\nkpSBgVqSJEnKwEAtSZIkZWCgliRJkjIwUEuSJEkZGKglSZKkDAzUkiRJUgYGakmSJCkDA7UkSZKU\ngYFakiRJysBALUmSJGVgoJYkSZIyMFBLkiRJGRioJUmSpAwM1JIkSVIGBmpJkiQpAwO1JEmSlIGB\nWpIkScqgotQbDCE8C8xLn/4nxnho3rwjgaOBOmBsjPEvpa5PktZWc+d+wOGHH8ill17JRhv1AmDO\nnPcZM2Zk/TIzZrzC4MHDGDRoH/bee3d69twAgC22+CaDBw/jb3+7j9tv/z0VFeVsssmmnHDCybRp\nk/StvPjidCZMGMf48RMBePXVyIUXnkt5eTkbbLAhJ588un7ZZcuWcdJJv+I739mJvfb6aQmPgiSt\nfUoaqEMIVUBZjHHnRuatDwwHtgKqgMdDCH+LMX5SyholaW1UV1fHBRecQ7t2lStM79p1nfoAPH36\nNCZOvIo99tib//73LTbf/KtccMGl9ct+8skirrlmAjfddBtVVVWMGTOSJ554jB13HMAtt9zI/fff\nS1VV+/rlr7vuGg499Ai2225HzjhjFE888Tg77rgTANdcM4H58+chSSr9kI++QHUI4YEQwkMhhP55\n87YBpsQYP4kxfgTMAL5Z4vokaa00fvxl7LXXPqyzzjqNzs/lclx66YWceOLJlJeXE+NLvP/+exx7\n7NGceOJwZs58g7Zt23H11ddRVVUFwNKlS+sDeo8ePTn77AtXaHPzzQPz5s0jl8tRW7uAioqkD+Yf\n//g7ZWVlbLvtdkXcY0lqPUo95KMWuAj4HbAZ8NcQQogx1gGdgI/ylp0PdF5Zg126VFNRUV6MWiVp\nrfDHP/6RHj3WY+DA3bjttkl06VJNt241Kyzz4IMP8rWvBfr16wNA794bMnToEH70ox/xzDPPcM45\np3PnnXey3nrJr9VJkyaxdOlidt/9e5SVlbHvvnvx1ltv0bZteX3bX//65px55pncfPP11NTUsNtu\nO/Pmm2/y6KMPMm7cOK688ko6dqz6TC1fBO+1dAEqWGnPz9oSbktZrOnzotSB+hVgRowxB7wSQpgD\nfAWYRTKuOn/vaoAPV9bg3LmevJI+32699XbKysp45JHHmDHjFU444UTOO+8Sunb9tLf6jjv+yL77\n/pzZs+cDsP76vejRozezZ89no40C77zzLu+9l/Q2X3XVOGbNepMzzjiX99//uL6NDz5YwJIlS+vb\nOOussVxxxUQ22aQ3d955O2PGnEX79lW89db/+MUvfsk777xNRUVbOnb8Mv37b1/agyIVaPn5LOVb\nnfOiuRBe6kB9GNAHGBpC6E7SK/12Ou8p4Ox0nHUl8DVgeonrk6S1zpVXXlP/eNiwozjppJErhGmA\nl19+iT59+tY/v+66iXTu3Jlf/vJgXn31FdZddz3Kysq44IKzadu2Leeee3H9Fwyb0qlTJzp06ADA\nOut044UXnueEE0bUz7/22t/StWtXw7SkL7xSB+prgRtCCI8DOZKAPTyEMCPGeHcIYRzwGMnY7lNj\njItKXJ8ktQoPPHAfCxfWMmjQT5g7dy4dOnSgrKysfv4BBxzCWWeNZurUKZSXl3PqqacT48v85S9/\nom/fbzF8+GAA9t33FwwYsEuj2xgxYjSnnz6S8vIKKioqGDFiVEn2TZJam7JcLtfSNWQye/b81r0D\nkqRWJ/fw+JYuQQUq23lYybb1yFkOQ20tBoyuXuV1unWrKWtqXsmvQy1JrcmjC89p6RJUoJ3aj1z5\nQpJUBN4pUZIkScrAQC1JkiRlYKCWJEmSMjBQS5IkSRkYqCVJkqQMDNSSJElSBgZqSZIkKQMDtSRJ\nkpSBgVqSJEnKwEAtSZIkZeCtx6W1yNKlSzn//LHMmvUmUMZJJ53CJptsWj//gQf+yq233kybNuX8\n+Md7svfePwXgsMN+SXV1BwC6d+/ByJFjePrpf3L11eMpLy9nq6224aijhvLkk09w88031Lc3bdpz\n3HTTbeRyOS644GwgR8+eGzJixCgqKpJfD3PnzmXIkMO58cbfU1lZWapDIUlSq2GgltYiU6Y8BsCE\nCdfx7LPPMHHiVZx33iX186+88jImTbqd9u2rOeCAfdl1192orKwkl8sxfvzEFdq66qrLOe20sfTq\ntTFDhx7Ba6/NoH//7enff3sAJk++iT59+tKr18accsoJHH30MWy55bc5++zTmTLlMQYM2IV//nMq\nV199BR98MKd0B0GSpFbGQC2tRXbaaWe2335HAN599x06dqxZYX7v3pvx8ccfU15eTi6Xo6ysjBkz\nXmXRokX8+tfHsHTpUo466hi22KIPm20WmDdvHnV1dSxevJg2bT4d4fXee+9y//33cs01NwEwduwF\nlJeXs2TJEubMmUPHjh0BaNOmjMsuu4rDDz+wREdAkqTWx0AtrWUqKioYO3YMjz76MGPHnr/CvI03\n7s3hhx9I+/bt2WmnXaipqaGqqopf/OJA9thjL2bNmsmJJw5n8uQ76d17U0aM+BWdOnWmd+/N2Gij\nXvXt3HbbLey33/60a9cOgPLyct55521+9auhdOjQkU033QyArbfuX7L9liSptfJLidJaaNSoM/j9\n7+/k/PPHsnDhQgBmzHiVqVMf54477uaOO+5m7twPeOihv7PBBhvygx/8iLKyMjbccCM6d+7MnDnv\nM2nSDUyadDu33/4nNthgA2699WYAli1bxpQpj/O97+22wjbXX/8r3HrrXey11z5cccWlJd9nSZJa\nKwO1tBa57757mDTpegCqqqpo06YNbdqUAdCxY0cqKyuprKykvLycLl2+zPz587jnnru54orLAHj/\n/dksWLCALl2+TPv21bRvXw1A167rMH/+fABef/01NtpoIyorq+q3O2LEr5k1ayYA1dXVKwwPkSRJ\nzXPIh7QWGTDgu5xzzhkcc8yR1NXVMXz48TzyyMMsXFjLoEE/YdCgnzB06OFUVLSlR4+e7L77HgCc\nffbpDBlyOGVlZZxyymm0a9eOYcN+xa9/fQyVlZV07NiRkSNPB2DmzDfp3r3nCts94IBDOOec06mo\naEtVVRUjRowu9a5LktRqleVyuZauIZPZs+e37h2QtFZ7dOE5LV2CCrRT+5El21bu4fEl25ayKdt5\nWMm29chZtSXblrIZMLp6ldfp1q2mrKl5fq4rSZIkZeCQDyk19Ol3W7oEFeiqrddr6RIkSapnD7Uk\nSZKUgYFakiRJysBALUmSJGVgoJYkSZIyMFBLkiRJGRioJUmSpAwM1JIkSVIGBmpJkiQpAwO1JEmS\nlIGBWpIkScrAQC1JkiRlYKCWJEmSMjBQS5IkSRkYqCVJkqQMDNSSJElSBgZqSZIkKQMDtSRJkpSB\ngVqSJEnKwEAtSZIkZWCgliRJkjIwUEuSJEkZGKglSZKkDAzUkiRJUgYGakmSJCkDA7UkSZKUQUUp\nNxZCaAtcB/QCKoGxMca78+b/GjgCmJ1OOjrGGEtZoyRJkrQqShqogQOAOTHGA0MIXwaeA+7Om98P\nOCjG+K8S1yVJkiStllIH6juAP6SPy4C6BvP7AaeEENYH7okxnlvK4iRJkqRVVdJAHWP8GCCEUEMS\nrEc1WORW4EpgHnBXCGFgjPEvzbXZpUs1FRXlxShXXzjvtnQBKlC3bjWl29jM0m1K2ZTyvHivZFtS\nViX9fUFtCbelLNb0eVHqHmpCCBsAdwFXxRgn500vAy6LMX6UPr8H+BbQbKCeO9eTV/qimT17fkuX\noLWQ54Ua43mhxqzOedFcCC/1lxLXAx4AhsUYH2wwuxMwPYTwNWAB8F2SLzBKkiRJa61S91CPBLoA\no0MIo9Np1wAdYowTQwgjgX8AnwAPxhjvLXF9kiRJ0iop9Rjq44Djmpk/CZhUuookSZKkbLyxiyRJ\nkpSBgVqSJEnKwEAtSZIkZWCgliRJkjIwUEuSJEkZGKglSZKkDAzUkiRJUgYGakmSJCkDA7UkSZKU\ngYFakiRJysBALUmSJGVgoJYkSZIyMFBLkiRJGRioJUmSpAwM1JIkSVIGBmpJkiQpAwO1JEmSlIGB\nWpIkScrAQC1JkiRlYKCWJEmSMjBQS5IkSRkYqCVJkqQMDNSSJElSBgZqSZIkKQMDtSRJkpSBgVqS\nJEnKwEAtSZIkZWCgliRJkjIwUEuSJEkZGKglSZKkDAzUkiRJUgYGakmSJCkDA7UkSZKUgYFakiRJ\nysBALUmSJGVgoJYkSZIyMFBLkiRJGVS0dAFfVMuWLePii89jxoxXadu2LSefPJqePTdYYZm5c+cy\nZMjh3Hjj76msrOTjjz9mzJiRLFxYS9u27TjttDPp2nUdnn/+/xg//jLKysrYcstvM3To8Po2Fi1a\nxODBhzF48DD699+eDz/8kDPOOJVPPvmEddbpxsiRY6iqquLmm2/g739/gA4dOrD//gexww7fKfUh\nkSRJapXsoW4hjz32MIsXL+a3v72ewYOPZfz4S1eY/89/TuX444/hgw/m1E+7994/07t3b6666nfs\nuuv3mTx5EgCXX34xZ5xxDhMn3sBLL73IK6+8XL/OJZecT1nZp+3ecMM1fP/7P+Sqq37HZpsF/vSn\nO3nttRn87W/389vfXs8ll4zn2muvZtGiRcU9AJIkSZ8TBuoWMm3ac2y77XYAbLFFH15++aUV5rdp\nU8Zll11Fp06d6qf17r0ptbW1ACxYsICKiuQDhokTb6B79x7U1tayYMHHtG9fDcDkyZPYYotvsumm\nmze63f79t+eZZ57ijTf+w7e+1Y/KykoqKyvp2XNDZsx4tXg7L0mS9DlioG4hCxYsoEOHjvXP27Rp\nQ11dXf3zrbfuT+fOX1phnU6dOvPUU09ywAH78vvfT2LgwEEAVFRUMH36Cxx00M/48pe7su666/LM\nM0/x1lsz2XPPvT+z3Y4dk+1WV1fz8ccf07v3pjz//LPU1i7go48+ZPr0aSxatLBYuy5JkvS54hjq\nFtKhQ4f63maAXC5X3+PclOuvv4b99z+IvfbahxkzXmXUqN9w4423Akkv9x/+8GcmTryKm2++kVmz\nZvLuu28zbNhRzJz5Jq+88jJdu3at325lZRW1tbXU1NTQq9fG7LPPfpxwwrGsu+76fP3r3/hMmJck\nSVLj7KFuIX369OXJJ6cAMH36C2yyyaYrXaempqa+d7lLly4sWLCAXC7H0KFHMG/ePCDpdS4rK+P0\n089mwoTrGD9+Ittuux1Dhgxns80Cffr0ZerUZLtPPvkE3/zmlsydO5fa2lomTLiOk04aybvvvssm\nm/Qu0p5LkiR9vthD3UJ22mkXnn76nwwefBi5XI6RI8dw660307PnBuy444BG1znyyCGcd95Z3HXX\nH6irq2PEiFMpKyvjF784gBNPHE67du3o2rUrI0aMbnK7Bx98OGPHns6f/3wXnTt/iTFjzqaqqoo3\n3vgPRxxxEG3bVnDMMcdRXl5enB2XJEn6nCnL5XItXUMms2fPb907oLXG0KffbekSVKCrtl6vZNt6\ndOE5JduWstmp/ciSbSv38PiSbUvZlO08rGTbeuSs2pUvpLXCgNHVq7xOt241ZU3Nc8iHJEmSlMEX\ncsjHgrOvaekSVKAOpx7Z0iVIkiQ1q6SBOoTQBrgK6At8AhwRY5yRN/9I4GigDhgbY/xLKeuTJEmS\nVlWph3zsBVT9//buP9aruo7j+PMWCegVFWpQIjZI39BMVxDj94gMl4axUYsBE0pGIOlQlmATJTbE\nXLkiKi1dqAk0ZnM4i2wQlIKUmQkir8QSUkYYkWYp8as/Pp+rB3cvCN97v7/u67Hdfc/3fM459312\nfr2/n/M55yNpMDAX+FZTQUT0AK4BhgKXAIsiomOZ4zMzMzMzOyHlTqiHAasBJD0ODCiUDQQek7Rf\n0ivAduDCMsdnZmZmZnZCyt2GugvwSuH7oYjoIOlgM2X/Bs443gKP9cRli/N8+7oTncXagZWXnl7p\nEKwKjWNRpUOwavT5GyodgVWhzy32daS9KncN9atAcW97V06mmys7HfhXuQIzMzMzMzsZ5U6oHwMu\nBYiIQcDmQtnvgOER0SkizgD6AVvKHJ+ZmZmZ2Qkpa8cuhbd8XAg0AF8kJdjbJa3Kb/mYRkr0b5H0\nQNmCMzMzMzM7CTXfU6KZmZmZWSW5p0QzMzMzsxI4oTYzMzMzK0G77HrcrF5ERCdgG/AgcLuknSe5\nnF7ARZIeas34rLpFxHxgN/A4cLmkBRGxW1KPykZmrS2fKyYBPYHdku44xrRzgbXAk8CvgI7AGOAS\nScvKEK5ZzXFCbVYHJM0qcRGjgL6AE+p2SNJTwFOVjsPaVA9gKrlztWORdCu8+UO7i6T+ETESuBxw\nQm3WDD+UWIUiojPwY+Bc4BTgOmAmcCbwAeB7kn4QEetItZN9SW9N+UIengP8D+gNrJC0MCKW5mnO\nARqBKyRtK+NqWSuJiEbgfuAsUo+io4AXgOnAeGAIaRtfCVwMTACOkPaFxRFxHnAXad/6by5fD5wK\nfEXSqnKuj7WOiJgCfInUlO9sUq1zH9LrR6cC3YB7SOeRBuAKYCKphnobMF3S+IjYB/ySdK54GrhK\nki8UNS4ifkS6RjSStu8ppH1inqSHImIHaT/YSjq3rACuIfVwvJx0PbkIuFHSD8u/BtYWcr5xLym3\n+BswAvgzsAfoStr2Y0h9g7wXWCDpgYj4DHAz6VzyJOn8cbj8a1A93Ia6Ok0HXpA0mJQg9SclQ6OB\n0aQEu8kGSSOBnwJfy+POBcYBg4DrC9M+L2kUMB+4rS1XwNrUdGCLpBHAnc2UPytpCG/9yBoGDAfG\nRkQA3wQW5f3rO6SL5K3AMifTNW+fpGGkhHqepIGkBGoscCOwKu8bs4GBLSyjMzBH0lBSwjWm7cO2\nMlhISpYXAC9J+iQwC5iRy88BJki6tjDPVcBWSV/O8691Ml13pgF/zcf7fKB7Hr9c0sXAIeA04FOk\n/OP2iOgALAEukzSAVLHTs9yBVxsn1NUpgI0Akp4jJctjI+InpIviewrTrs2fG/J8AJslHZT0H+D1\n40xrted8UkdISNoEHHhbufLnBaQfV2vyXzfgPI7ev1ZJeqQMMVt5NG37nZK25+Gm47243TdIur+F\nZeyUtONt81p9+UP+3E26MwXwD0l7KxSPVU4/0nFOvmv9ch6vwjTrJR2W9HdgH/A+0o/3PXm+2072\n+Z164oS6Oj0LfBwgInoD3wU2SpoErCTVPDbpnz+HAs/k4ZZuzzY3rdWercBggIj4KEf/wAJouu0m\n0nb+RL6LsZR0C7+4f02MiKvzPD4f1L6mbX92RDQ9WNh0vBe3+4iI+EYLy+gZEe/Pw8Nwj7X1oniM\nN3eNON7tep8j6tMW3rqe9CE164Cj94f+ubw70IXUHOTMiOiaxy+OiJbueLUbPjiq051A74hYT2rb\ntBGYmb/PAg5GRMc87ZQ8/jLSLblj+XRErCU1A5ndNqFbGdxB2j8eJbWt39/cRJL+RKqZfjQiniDV\nTr8EfBW4IbfBn0hqj70Z+GxEjG/78K0M9gNLImITsIv0sOktpG28Dvg6zTcXAtgLLI6IjcAOSb8o\nQ7zW9vaQ2k13Psn5nwc+EhGlPgBt1eVu4IMR8RtSk483mpmmR0SsAR4mPVNxiNQc6OF8HWoAfl+m\neKuWH0qsYfnCOP2dPFyYH0pcIem4T3ibWW3zq+/M7J2IiCFAo6RH8gPrqyX1KZRPAfpKmlupGGuF\nX5tnZmZm1j79BVgeETeTmg/OrHA8Ncs11GZmZmZmJXAbajMzMzOzEjihNjMzMzMrgRNqMzMzM7MS\nOKE2M6sxETEyv+WnOG5ARNyVh9dFxMhKxGZm1h75LR9mZnVA0hPA1ErHYWbWHjmhNjOrA7lGen7u\nFRNgWkTcTup04VpJ6yJiPjAI6AUsIfWguJDU/fRZwPWSVub31ncDPgTMBeZIGpL/z2RgkKQZZVo1\nM7Oq5yYfZmb16TVJHwMmA/cVelftJOnDkr4PXA1MzdNdCdxUmH+vpH7AKlJPaU2dPUwmdWNvZmaZ\nE2ozs/p0N4Ckp4GXgb55/KbCNJOACyJiHjAbaCyUbcrzHwHuASZFRC+gu6TiMszM2j03+TAzq08H\nC8MNwIE8/Hph/G+BXwPrgDXAskJZcbqlwGrgDeDeVo7TzKzmuYbazKw+TYT09g+gC/BcsTAiugLn\nAzdJ+jkwGnh3cwuStAN4EZgB3NeGMZuZ1STXUJuZ1abhEfFa4fuLwO7C98aI+CNwCJgg6UBEvFko\n6Z/5NXvPRMSrwEbg1Ig4rYX/twIYJ2lXq66FmVkdaDhy5EilYzAzsyoWER1INdMrJf2s0vGYmVUb\nN/kwM7MWRUQDsAs4DDxY4XDMzKqSa6jNzMzMzErgGmozMzMzsxI4oTYzMzMzK4ETajMzMzOzEjih\nNjMzMzMrgRNqMzMzM7MSOKE2MzMzMyvB/wGZcp28RT3UAwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f507a3b7ef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 8))\n",
+    "g = sns.barplot(x='library', y='cpu_time', data=all, order=libs)\n",
+    "g.set(ylim=(0, 20))\n",
+    "sns.plt.title('get_structs: cpu times per iteration on a linear scale (smaller is better)')\n",
+    "g.set(xlabel='Library', ylabel='Iteration time (ms)')\n",
+    "for p in g.patches:\n",
+    "    g.annotate(str(p.get_height()), (p.get_x() + p.get_width() / 2, p.get_height() + 0.0005), ha='center', va='bottom')\n",
+    "g.get_figure().savefig('plots/get_structs_cpu_linear.svg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 377,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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w0Ucfcu21V3PMMSdUt+3SpQtf+cpX+eSTT5g37xPatctiwtSpLzBo0DcA2Gqr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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f507ab114e0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 8))\n",
+    "g = sns.barplot(x='library', y='real_time', data=all, order=libs)\n",
+    "g.set(ylim=(0, 20))\n",
+    "sns.plt.title('get_structs: wall clock times per iteration on a linear scale (smaller is better)')\n",
+    "g.set(xlabel='Library', ylabel='Iteration time (ms)')\n",
+    "for p in g.patches:\n",
+    "    g.annotate(str(p.get_height()), (p.get_x() + p.get_width() / 2, p.get_height() + 0.0005), ha='center', va='bottom')\n",
+    "g.get_figure().savefig('plots/get_structs_real_linear.svg')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/run_benchmarks.sh b/run_benchmarks.sh
new file mode 100755
index 0000000..ba4dcf1
--- /dev/null
+++ b/run_benchmarks.sh
@@ -0,0 +1,8 @@
+#! /bin/sh
+
+# This script assumes that the executable is stored in ./build/bin/rpclib_benchmark,
+# relative to the script itself.
+
+SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
+
+$SCRIPT_DIR/build/bin/rpclib_benchmark --benchmark_min_time=3.0 --benchmark_repetitions=5 --benchmark_out=benchmark.json --benchmark_out_format=json
diff --git a/src/benchmark.cc b/src/benchmark.cc
index 155a6b7..75cb122 100644
--- a/src/benchmark.cc
+++ b/src/benchmark.cc
@@ -36,14 +36,14 @@ int capnp_bench::blob_size_ = 0;
 
 constexpr std::size_t min_size = 1 << 10;
 constexpr std::size_t max_size = 16 << 10 << 10;
-constexpr std::size_t grpc_max_size = 1 << 10 << 10; // https://github.com/grpc/grpc/issues/9510
+// constexpr std::size_t grpc_max_size = 1 << 10 << 10; // https://github.com/grpc/grpc/issues/9510
 constexpr std::size_t multiplier = 2;
 
 MAKE_BENCHMARK(direct_bench, get_blob)->RangeMultiplier(multiplier)->Range(min_size, max_size);
 MAKE_BENCHMARK(rpclib_bench, get_blob)->RangeMultiplier(multiplier)->Range(min_size, max_size);
 MAKE_BENCHMARK(capnp_bench, get_blob)->RangeMultiplier(multiplier)->Range(min_size, max_size);
 MAKE_BENCHMARK(thrift_bench, get_blob)->RangeMultiplier(multiplier)->Range(min_size, max_size);
-MAKE_BENCHMARK(grpc_bench, get_blob)->RangeMultiplier(multiplier)->Range(min_size, grpc_max_size);
+// MAKE_BENCHMARK(grpc_bench, get_blob)->RangeMultiplier(multiplier)->Range(min_size, grpc_max_size);
 
 MAKE_BENCHMARK0(direct_bench, get_structs);
 MAKE_BENCHMARK0(rpclib_bench, get_structs);
@@ -59,9 +59,8 @@ MAKE_BENCHMARK0(grpc_bench, get_answer);
 
 int main(int argc, char *argv[]) {
   printf("Initalizing blob cache...\n");
-  for (std::size_t s = min_size; s < max_size; ++s) {
+  for (std::size_t s = min_size; s <= max_size; s *= multiplier) {
     get_blob(s);
-    s *= multiplier;
   }
 
   printf("Initalizing struct cache...\n");
diff --git a/src/target_code.cc b/src/target_code.cc
index b90b099..4e33f43 100644
--- a/src/target_code.cc
+++ b/src/target_code.cc
@@ -1,17 +1,14 @@
 #include "target_code.h"
-#include <chrono>
 #include <map>
 #include <stdlib.h>
-#include <thread>
 
 int get_answer(int num) {
-  std::this_thread::sleep_for(std::chrono::milliseconds(20));
   return 41 + num;
 }
 
 static std::map<int, std::string> blob_cache;
 
-std::string get_blob(int size) {
+const std::string& get_blob(int size) {
   if (blob_cache.find(size) != end(blob_cache)) {
     return blob_cache[size];
   }
@@ -22,8 +19,8 @@ std::string get_blob(int size) {
     c = static_cast<unsigned char>(rand() % 256);
   }
 
-  blob_cache[size] = s;
-  return s;
+  blob_cache[size] = std::move(s);
+  return blob_cache[size];
 }
 
 std::string rand_str(std::size_t size) {
