diff --git a/README.rst b/README.rst
index e1eda6e6..63499e60 100644
--- a/README.rst
+++ b/README.rst
@@ -115,24 +115,16 @@ Performance
 To run the benchmarks, execute the included bench/bench_zfec.py script with
 optional --k= and --m= arguments.
 
-On my Athlon 64 2.4 GHz workstation (running Linux), the "zfec" command-line
-tool encoded a 160 MB file with m=100, k=94 (about 6% redundancy) in 3.9
-seconds, where the "par2" tool encoded the file with about 6% redundancy in
-27 seconds.  zfec encoded the same file with m=12, k=6 (100% redundancy) in
-4.1 seconds, where par2 encoded it with about 100% redundancy in 7 minutes
-and 56 seconds.
-
-The underlying C library in benchmark mode encoded from a file at about 4.9
-million bytes per second and decoded at about 5.8 million bytes per second.
-
-On Peter's fancy Intel Mac laptop (2.16 GHz Core Duo), it encoded from a file
-at about 6.2 million bytes per second.
-
-On my even fancier Intel Mac laptop (2.33 GHz Core Duo), it encoded from a
-file at about 6.8 million bytes per second.
-
-On my old PowerPC G4 867 MHz Mac laptop, it encoded from a file at about 1.3
-million bytes per second.
+Here's the results for an i7-12700k:
+
+```
+measuring encoding of data with K=3, M=10, encoding 1000000 bytes 1000 times in a row...
+Average MB/s: 364
+measuring decoding of primary-only data with K=3, M=10, 1000 times in a row...
+Average MB/s: 1894750
+measuring decoding of secondary-only data with K=3, M=10, 1000 times in a row...
+Average MB/s: 3298
+```
 
 Here is a paper analyzing the performance of various erasure codes and their
 implementations, including zfec:
diff --git a/bench/bench_zfec.py b/bench/bench_zfec.py
index 9a9b8e2f..7e9fbe79 100644
--- a/bench/bench_zfec.py
+++ b/bench/bench_zfec.py
@@ -1,9 +1,8 @@
-from zfec import easyfec, Encoder, filefec
+from zfec import easyfec, Encoder, filefec, Decoder
 from pyutil import mathutil
 
 import os, sys
-
-from pyutil import benchutil
+from time import time
 
 FNAME="benchrandom.data"
 
@@ -16,7 +15,7 @@ def donothing(results, reslenthing):
 K=3
 M=10
 
-d = ""
+d = b""
 ds = []
 easyfecenc = None
 fecenc = None
@@ -30,12 +29,12 @@ def _make_new_rand_data(size, k, m):
     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]))
+    ds[-1] = ds[-1] + b"\x00" * (len(ds[-2]) - len(ds[-1]))
     easyfecenc = easyfec.Encoder(k, m)
     fecenc = Encoder(k, m)
 
-import sha
-hashers = [ sha.new() for i in range(M) ]
+from hashlib import sha256
+hashers = [ sha256() for i in range(M) ]
 def hashem(results, reslenthing):
     for i, result in enumerate(results):
         hashers[i].update(result)
@@ -77,26 +76,46 @@ def _encode_data_fec(N):
 
 def bench(k, m):
     SIZE = 10**6
-    MAXREPS = 64
+    MAXREPS = 1000
     # 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,]:
-    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)
+    print("measuring encoding of data with K=%d, M=%d, 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)
+            _init_func(BSIZE)
+            start = time()
+            for _ in range(MAXREPS):
+                f(BSIZE)
+            elapsed = (time() - start) / MAXREPS
+            print("Average MB/s:", (BSIZE / (1024 * 1024)) / elapsed)
+
+    print("measuring decoding of primary-only data with K=%d, M=%d, %d times in a row..." % (k, m, MAXREPS))
+    blocks = fecenc.encode(ds)
+    sharenums = list(range(len(blocks)))
+    decer = Decoder(k, m)
+    start = time()
+    for _ in range(MAXREPS):
+        decer.decode(blocks[:k], sharenums[:k])
+    assert b"".join(decer.decode(blocks[:k], sharenums[:k]))[:SIZE] == b"".join(ds)[:SIZE]
+    elapsed = (time() - start) / MAXREPS
+    print("Average MB/s:", (sum(len(b) for b in blocks) / (1024 * 1024)) / elapsed)
+
+    print("measuring decoding of secondary-only data with K=%d, M=%d, %d times in a row..." % (k, m, MAXREPS))
+    blocks = fecenc.encode(ds)
+    sharenums = list(range(len(blocks)))
+    decer = Decoder(k, m)
+    start = time()
+    for _ in range(MAXREPS):
+        decer.decode(blocks[k:k+k], sharenums[k:k+k])
+    assert b"".join(decer.decode(blocks[k:k+k], sharenums[k:k+k]))[:SIZE] == b"".join(ds)[:SIZE]
+    elapsed = (time() - start) / MAXREPS
+    print("Average MB/s:", (sum(len(b) for b in blocks) / (1024 * 1024)) / elapsed)
+
 
 k = K
 m = M