random bits and pieces

concurrent-read/Makefile

@@ -0,0 +1,35 @@
+ifeq (debian, $(shell hostname))
+ITEMS=100
+else
+ITEMS=3000
+endif
+
+ARGS=\
+	"normal		none	none		sort	pread"	\
+	"normal		sort	fadvise		none	pread"	\
+	"normal		sort	readahead	none	pread"
+
+
+B=\
+	"normal		sort	fadvise	rand	gthread512"	\
+	"normal		sort	fadvise	sort	gthread512"
+
+
+all: read_bench random.bin
+	@for ARGS in $(ARGS); do					\
+		sync;							\
+		sudo bash -c "echo 3 > /proc/sys/vm/drop_caches";	\
+		sync;							\
+		sudo dd if=/dev/sda of=/dev/null bs=1M count=1 2> /dev/null;	\
+		./read_bench $(ITEMS) $$ARGS;				\
+	done
+
+
+random.bin:
+	dd if=/dev/urandom of=random.bin bs=1M count=16
+
+
+
+read_bench: read_bench.c
+	gcc -g -O2 -Wall read_bench.c -o read_bench -lm -lrt -I/usr/lib64/glib-2.0/include -I/usr/lib/glib-2.0/include -I/usr/include/glib-2.0 -lgthread-2.0
+

concurrent-read/README

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+  * different options for fadvise - random? dontuse? +
+  * visualize input data
+  * random and sorted fadvise
+

concurrent-read/hist.py

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+#!/usr/bin/env python
+import numpy as np
+import matplotlib.mlab as mlab
+import matplotlib.pyplot as plt
+
+x = []
+for line in open("norm.csv"):
+    x.append( float(line) )
+
+
+# the histogram of the data
+n, bins, patches = plt.hist(x, 50, normed=1, facecolor='green', alpha=0.75)
+
+# add a 'best fit' line
+y = mlab.normpdf( bins, 0, 5)
+l = plt.plot(bins, y, 'r--', linewidth=1)
+
+plt.xlabel('Smarts')
+plt.ylabel('Probability')
+plt.title(r'$\mathrm{Histogram\ of\ IQ:}\ \mu=100,\ \sigma=15$')
+plt.axis([-5, 5, 0, 0.8])
+plt.grid(True)
+
+plt.show()