add larson's server benchmark

Makefile

@@ -14,7 +14,7 @@ CXX = g++
 WARNFLAGS = -Wall -Wextra
 COMMONFLAGS = -fno-builtin -fPIC -DPIC -pthread
 OPTFLAGS = -O3 -DNDEBUG
-#OPTFLAGS = -O0 -g3
+# OPTFLAGS = -O0 -g3
 
 CXXFLAGS = -std=c++11 -I. $(OPTFLAGS) $(WARNFLAGS) $(COMMONFLAGS) -fno-exceptions
 CFLAGS = -I. $(OPTFLAGS) $(WARNFLAGS) $(COMMONFLAGS)
@@ -48,6 +48,14 @@ $(OBJDIR)/trace_run-glibc-notc: $(OBJDIR)/trace_run $(MAKEFILE_LIST)
 	patchelf --set-interpreter $(GLIBC_NOTC)/ld-linux-x86-64.so.2 $@
 	patchelf --set-rpath $(GLIBC_NOTC) $@
 
+$(OBJDIR)/larson: $(OBJDIR)/larson.o
+	$(CXX) -pthread -o $@ $^
+
+$(OBJDIR)/larson-glibc-notc: $(OBJDIR)/larson
+	cp $< $@
+	patchelf --set-interpreter $(GLIBC_NOTC)/ld-linux-x86-64.so.2 $@
+	patchelf --set-rpath $(GLIBC_NOTC) $@
+
 $(OBJDIR)/cache-thrash: $(OBJDIR)/cache-thrash.o
 	$(CXX) -pthread -o $@ $^
 

bench.py

@@ -10,6 +10,7 @@ from loop import loop
 # from bench_conprod import conprod
 from mysql import mysql
 from dj_trace import dj_trace
+from larson import larson
 
 parser = argparse.ArgumentParser(description="benchmark memory allocators")
 parser.add_argument("-s", "--save", help="save benchmark results to disk", action='store_true')
@@ -22,7 +23,7 @@ parser.add_argument("-sd", "--summarydir", help="directory where all plots and t
 parser.add_argument("-a", "--analyse", help="collect allocation sizes", action='store_true')
 
 
-benchmarks = [loop, mysql, falsesharing, dj_trace]
+benchmarks = [loop, mysql, falsesharing, dj_trace, larson]
 
 def main():
     args = parser.parse_args()

larson.py

+import csv
+import pickle
+import matplotlib.pyplot as plt
+import multiprocessing
+import numpy as np
+import os
+import re
+import subprocess
+
+from benchmark import Benchmark
+
+throughput_re = re.compile("^Throughput =\s*(?P<throughput>\d+) operations per second.$")
+
+class Benchmark_Larson( Benchmark ):
+    def __init__(self):
+        self.name = "larson"
+        self.descrition = """This benchmark is courtesy of Paul Larson at Microsoft
+                             Research. It simulates a server: each thread allocates
+                             and deallocates objects, and then transfers some objects
+                             (randomly selected) to other threads to be freed."""
+
+        self.cmd = "build/larson{binary_suffix} 1 8 {maxsize} 1000 10000 1 {threads}"
+
+        self.args = {
+                        "maxsize" : [8, 32, 64, 128, 256, 512, 1024],
+                        "threads" : range(1, multiprocessing.cpu_count() * 2 + 1)
+                    }
+
+        self.requirements = ["build/larson"]
+        super().__init__()
+
+    def process_stdout(self, result, stdout, verbose):
+        for l in stdout.splitlines():
+            res = throughput_re.match(l)
+            if res:
+                result["throughput"] = int(res.group("throughput"))
+                return
+        print(stdout)
+        print("no match")
+
+    def summary(self, sd=None):
+        # Speedup thrash
+        args = self.results["args"]
+        nthreads = args["threads"]
+        targets = self.results["targets"]
+
+        sd = sd or ""
+
+        for arg in args:
+            loose_arg = [a for a in args if a != arg][0]
+            for arg_value in args[arg]:
+                for target in targets:
+                    y_vals = []
+                    for perm in self.iterate_args_fixed({arg : arg_value}, args=args):
+                        d = [m["throughput"] for m in self.results[target][perm]]
+                        y_vals.append(np.mean(d))
+                    x_vals = list(range(1, len(y_vals) + 1))
+                    plt.plot(x_vals, y_vals, marker='.', linestyle='-',
+                        label=target, color=targets[target]["color"])
+                plt.legend()
+                plt.xticks(x_vals, args[loose_arg])
+                plt.xlabel(loose_arg)
+                plt.ylabel("OPS/s")
+                plt.title("Larson: " + arg + " " + str(arg_value))
+                plt.savefig(os.path.join(sd, ".".join([self.name, arg, str(arg_value), "png"])))
+                plt.clf()
+
+
+larson = Benchmark_Larson()