stick a .gitignore file

[tahoe-lafs/zfec.git] / zfec / bench / bench_zfec.py

diff --git a/zfec/bench/bench_zfec.py b/zfec/bench/bench_zfec.py
index 30b847c5be34699cc3537b6b78b229da9609d6af..9a9b8e2f7f3b28e33a26268189c426a3709cdb35 100644 (file)
--- a/zfec/bench/bench_zfec.py
+++ b/zfec/bench/bench_zfec.py
@@ -1,7 +1,7 @@
 from zfec import easyfec, Encoder, filefec
 from pyutil import mathutil
 
-import os
+import os, sys
 
 from pyutil import benchutil
 
@@ -20,17 +20,19 @@ d = ""
 ds = []
 easyfecenc = None
 fecenc = None
-def _make_new_rand_data(size):
-    global d, easyfecenc, fecenc
+def _make_new_rand_data(size, k, m):
+    global d, easyfecenc, fecenc, K, M
+    K = k
+    M = m
     d = os.urandom(size)
     del ds[:]
-    ds.extend([None]*K)
-    blocksize = mathutil.div_ceil(size, K)
-    for i in range(K):
+    ds.extend([None]*k)
+    blocksize = mathutil.div_ceil(size, k)
+    for i in range(k):
         ds[i] = d[i*blocksize:(i+1)*blocksize]
     ds[-1] = ds[-1] + "\x00" * (len(ds[-2]) - len(ds[-1]))
-    easyfecenc = easyfec.Encoder(K,M)
-    fecenc = Encoder(K,M)
+    easyfecenc = easyfec.Encoder(k, m)
+    fecenc = Encoder(k, m)
 
 import sha
 hashers = [ sha.new() for i in range(M) ]
@@ -40,10 +42,10 @@ def hashem(results, reslenthing):
 
 def _encode_file(N):
     filefec.encode_file(open(FNAME, "rb"), donothing, K, M)
-   
+
 def _encode_file_stringy(N):
     filefec.encode_file_stringy(open(FNAME, "rb"), donothing, K, M)
-   
+
 def _encode_file_stringy_easyfec(N):
     filefec.encode_file_stringy_easyfec(open(FNAME, "rb"), donothing, K, M)
 
@@ -57,11 +59,15 @@ def _encode_file_and_hash(N):
     filefec.encode_file(open(FNAME, "rb"), hashem, K, M)
 
 def _encode_data_not_really(N):
-    i = 0
-    for c in d:
-        i += 1
-    assert len(d) == N == i
-    pass
+    # This function is to see how long it takes to run the Python code
+    # that does this benchmarking and accounting and so on but not
+    # actually do any erasure-coding, in order to get an idea of how
+    # much overhead there is in using Python.  This exercises the
+    # basic behavior of allocating buffers to hold the secondary
+    # shares.
+    sz = N // K
+    for i in range(M-K):
+        x = '\x00' * sz
 
 def _encode_data_easyfec(N):
     easyfecenc.encode(d)
@@ -69,13 +75,35 @@ def _encode_data_easyfec(N):
 def _encode_data_fec(N):
     fecenc.encode(ds)
 
-def bench():
+def bench(k, m):
+    SIZE = 10**6
+    MAXREPS = 64
     # for f in [_encode_file_stringy_easyfec, _encode_file_stringy, _encode_file, _encode_file_not_really,]:
     # for f in [_encode_file,]:
     # for f in [_encode_file_not_really, _encode_file_not_really_and_hash, _encode_file, _encode_file_and_hash,]:
     # for f in [_encode_data_not_really, _encode_data_easyfec, _encode_data_fec,]:
-    for f in [_encode_data_fec,]:
-        for BSIZE in [2**22]:
-            benchutil.rep_bench(f, n=BSIZE, initfunc=_make_new_rand_data, MAXREPS=64, MAXTIME=None)
-
-bench()
+    print "measuring encoding of data with K=%d, M=%d, reporting results in nanoseconds per byte after encoding %d bytes %d times in a row..." % (k, m, SIZE, MAXREPS)
+    # for f in [_encode_data_fec, _encode_data_not_really]:
+    for f in [_encode_data_fec]:
+        def _init_func(size):
+            return _make_new_rand_data(size, k, m)
+        for BSIZE in [SIZE]:
+            results = benchutil.rep_bench(f, n=BSIZE, initfunc=_init_func, MAXREPS=MAXREPS, MAXTIME=None, UNITS_PER_SECOND=1000000000)
+            print "and now represented in MB/s..."
+            print
+            best = results['best']
+            mean = results['mean']
+            worst = results['worst']
+            print "best:  % 4.3f MB/sec" % (10**3 / best)
+            print "mean:  % 4.3f MB/sec" % (10**3 / mean)
+            print "worst: % 4.3f MB/sec" % (10**3 / worst)
+
+k = K
+m = M
+for arg in sys.argv:
+    if arg.startswith('--k='):
+        k = int(arg[len('--k='):])
+    if arg.startswith('--m='):
+        m = int(arg[len('--m='):])
+
+bench(k, m)