[pypy-dev] Twisted's deferToThread is much more slower with pypy than on cpython

[Nagy, Attila]

Hi,

Consider this example program:
https://gist.github.com/bra-fsn/1fd481b44590a939e849cb9073ba1a41

# pypy /tmp/deft.py 1
deferToThread: avg 1310.90 us, sync: avg 1.13 us, 1155.26x increase
deferToThread: avg 248.42 us, sync: avg 0.19 us, 1288.78x increase
deferToThread: avg 288.20 us, sync: avg 0.20 us, 1450.80x increase
deferToThread: avg 366.34 us, sync: avg 0.20 us, 1860.96x increase
deferToThread: avg 300.38 us, sync: avg 0.20 us, 1510.52x increase
deferToThread: avg 341.94 us, sync: avg 0.20 us, 1751.63x increase
deferToThread: avg 282.96 us, sync: avg 0.20 us, 1432.18x increase
deferToThread: avg 232.87 us, sync: avg 0.20 us, 1185.64x increase
deferToThread: avg 350.51 us, sync: avg 0.20 us, 1751.06x increase
deferToThread: avg 379.09 us, sync: avg 0.20 us, 1903.13x increase

While this runs, it uses only around 15% CPU.

Running this on more threads yields even worse results:
# pypy /tmp/deft.py 8
deferToThread: avg 8236.71 us, sync: avg 0.28 us, 29359.01x increase
deferToThread: avg 8286.90 us, sync: avg 0.32 us, 26029.29x increase
deferToThread: avg 8345.67 us, sync: avg 0.67 us, 12436.32x increase
deferToThread: avg 8685.53 us, sync: avg 0.33 us, 26617.55x increase
deferToThread: avg 8689.54 us, sync: avg 0.48 us, 18093.90x increase
deferToThread: avg 8743.51 us, sync: avg 0.31 us, 28124.92x increase
deferToThread: avg 8760.89 us, sync: avg 0.32 us, 27270.95x increase
deferToThread: avg 8757.82 us, sync: avg 0.32 us, 27791.52x increase
deferToThread: avg 5884.83 us, sync: avg 0.22 us, 26213.80x increase
deferToThread: avg 5927.90 us, sync: avg 0.23 us, 25773.31x increase
deferToThread: avg 6410.40 us, sync: avg 0.23 us, 27943.53x increase
deferToThread: avg 6133.28 us, sync: avg 0.23 us, 26417.04x increase
deferToThread: avg 6182.15 us, sync: avg 0.23 us, 26529.44x increase
deferToThread: avg 6679.73 us, sync: avg 0.22 us, 30022.54x increase
deferToThread: avg 6390.30 us, sync: avg 0.22 us, 28647.95x increase
deferToThread: avg 6509.35 us, sync: avg 0.23 us, 28201.91x increase
deferToThread: avg 6640.61 us, sync: avg 0.23 us, 28499.73x increase
deferToThread: avg 6508.86 us, sync: avg 0.23 us, 28293.32x increase
deferToThread: avg 6394.18 us, sync: avg 0.23 us, 27980.45x increase
deferToThread: avg 6584.74 us, sync: avg 0.23 us, 28983.66x increase
deferToThread: avg 6806.19 us, sync: avg 0.24 us, 28535.81x increase

The process now only uses around 2% CPU.

Running on even more threads:
# pypy /tmp/deft.py 16
deferToThread: avg 27107.54 us, sync: avg 0.29 us, 94991.56x increase
deferToThread: avg 28288.76 us, sync: avg 0.29 us, 96298.54x increase
deferToThread: avg 28365.01 us, sync: avg 3.86 us, 7346.72x increase
deferToThread: avg 28927.80 us, sync: avg 0.33 us, 86955.17x increase
deferToThread: avg 28958.93 us, sync: avg 0.29 us, 101428.44x increase
deferToThread: avg 29037.54 us, sync: avg 0.30 us, 98424.06x increase
deferToThread: avg 29223.17 us, sync: avg 0.29 us, 99730.33x increase
deferToThread: avg 29270.21 us, sync: avg 0.37 us, 78388.17x increase
deferToThread: avg 29312.24 us, sync: avg 0.32 us, 90785.14x increase
deferToThread: avg 29462.58 us, sync: avg 0.29 us, 101798.22x increase
deferToThread: avg 29475.06 us, sync: avg 0.32 us, 91309.64x increase
deferToThread: avg 29618.17 us, sync: avg 0.28 us, 105142.35x increase
deferToThread: avg 29781.02 us, sync: avg 0.32 us, 93284.17x increase
deferToThread: avg 29986.18 us, sync: avg 3.21 us, 9327.75x increase
deferToThread: avg 30258.60 us, sync: avg 0.42 us, 71364.92x increase
deferToThread: avg 30525.20 us, sync: avg 0.28 us, 107570.08x increase

This eats between 0.2-1% CPU.

And now on cypthon:
# python /tmp/deft.py 1
deferToThread: avg 316.17 us, sync: avg 1.38 us, 228.71x increase
deferToThread: avg 312.92 us, sync: avg 1.38 us, 226.96x increase
deferToThread: avg 320.22 us, sync: avg 1.39 us, 230.37x increase
deferToThread: avg 317.33 us, sync: avg 1.35 us, 235.24x increase

# python /tmp/deft.py 8
deferToThread: avg 2542.90 us, sync: avg 1.37 us, 1854.14x increase
deferToThread: avg 2544.50 us, sync: avg 1.35 us, 1878.13x increase
deferToThread: avg 2544.47 us, sync: avg 1.36 us, 1864.52x increase
deferToThread: avg 2544.52 us, sync: avg 1.38 us, 1839.01x increase
deferToThread: avg 2544.92 us, sync: avg 1.36 us, 1871.81x increase
deferToThread: avg 2546.71 us, sync: avg 1.39 us, 1830.35x increase
deferToThread: avg 2552.38 us, sync: avg 1.35 us, 1893.17x increase
deferToThread: avg 2552.40 us, sync: avg 1.36 us, 1870.20x increase

# python /tmp/deft.py 16
deferToThread: avg 4745.76 us, sync: avg 1.26 us, 3770.11x increase
deferToThread: avg 4748.67 us, sync: avg 1.24 us, 3817.03x increase
deferToThread: avg 4749.81 us, sync: avg 1.26 us, 3756.39x increase
deferToThread: avg 4749.72 us, sync: avg 1.24 us, 3839.88x increase
deferToThread: avg 4749.87 us, sync: avg 1.28 us, 3709.99x increase
deferToThread: avg 4752.63 us, sync: avg 1.24 us, 3842.90x increase
deferToThread: avg 4752.53 us, sync: avg 1.23 us, 3866.08x increase
deferToThread: avg 4752.55 us, sync: avg 1.23 us, 3855.40x increase
deferToThread: avg 4754.03 us, sync: avg 1.29 us, 3678.09x increase
deferToThread: avg 4754.97 us, sync: avg 1.25 us, 3817.19x increase
deferToThread: avg 4755.45 us, sync: avg 1.32 us, 3593.28x increase
deferToThread: avg 4756.35 us, sync: avg 1.25 us, 3804.18x increase
deferToThread: avg 4756.19 us, sync: avg 1.29 us, 3687.73x increase
deferToThread: avg 4757.19 us, sync: avg 1.23 us, 3860.74x increase
deferToThread: avg 4758.02 us, sync: avg 1.24 us, 3824.33x increase
deferToThread: avg 4759.63 us, sync: avg 1.24 us, 3830.40x increase

The interpreter uses around 100% CPU.

Python 2.7.11
PyPy 5.1.1
FreeBSD 10/amd64

Why deferToThread is so slow is another question, but here I'm 
interested in pypy's abysmal performance.