mirror of
https://github.com/stevenrobertson/cuburn.git
synced 2025-02-05 11:40:04 -05:00
133 lines
4.4 KiB
Python
133 lines
4.4 KiB
Python
import time
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import pycuda.autoinit
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import pycuda.compiler
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import pycuda.driver as cuda
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import numpy as np
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import sys, os
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os.environ['PATH'] = ('/usr/x86_64-pc-linux-gnu/gcc-bin/4.4.6:'
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+ os.environ['PATH'])
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with open('sortbench.cu') as f: src = f.read()
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mod = pycuda.compiler.SourceModule(src, keep=True)
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def launch(name, *args, **kwargs):
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fun = mod.get_function(name)
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if kwargs.pop('l1', False):
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fun.set_cache_config(cuda.func_cache.PREFER_L1)
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if not kwargs.get('stream'):
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kwargs['time_kernel'] = True
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print 'launching %s with %sx%s... ' % (name, kwargs['block'],
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kwargs['grid']),
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t = fun(*args, **kwargs)
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if t:
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print 'done (%g secs).' % t
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else:
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print 'done.'
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def go(scale, block, test_cpu):
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data = np.fromstring(np.random.bytes(scale*block), dtype=np.uint8)
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print 'Done seeding'
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if test_cpu:
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a = time.time()
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cpu_pfxs = np.array([np.sum(data == v) for v in range(256)])
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b = time.time()
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print cpu_pfxs
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print 'took %g secs on CPU' % (b - a)
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shmem_pfxs = np.zeros(256, dtype=np.int32)
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launch('prefix_scan_8_0_shmem',
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cuda.In(data), np.int32(block), cuda.InOut(shmem_pfxs),
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block=(32, 16, 1), grid=(scale, 1), l1=1)
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if test_cpu:
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print 'it worked? %s' % (np.all(shmem_pfxs == cpu_pfxs))
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shmeml_pfxs = np.zeros(256, dtype=np.int32)
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launch('prefix_scan_8_0_shmem_lessconf',
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cuda.In(data), np.int32(block), cuda.InOut(shmeml_pfxs),
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block=(32, 32, 1), grid=(scale, 1), l1=1)
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print 'it worked? %s' % (np.all(shmeml_pfxs == shmem_pfxs))
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popc_pfxs = np.zeros(256, dtype=np.int32)
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launch('prefix_scan_8_0_popc',
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cuda.In(data), np.int32(block), cuda.InOut(popc_pfxs),
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block=(32, 16, 1), grid=(scale, 1), l1=1)
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popc5_pfxs = np.zeros(32, dtype=np.int32)
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launch('prefix_scan_5_0_popc',
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cuda.In(data), np.int32(block), cuda.InOut(popc5_pfxs),
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block=(32, 16, 1), grid=(scale, 1), l1=1)
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def rle(a):
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pos, = np.where(np.diff(a))
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lens = np.diff(np.concatenate((pos, [len(a)])))
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return [(a[p], p, l) for p, l in zip(pos, lens)[:5000]]
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def go_sort(count, stream=None):
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data = np.fromstring(np.random.bytes(count), dtype=np.uint8)
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ddata = cuda.to_device(data)
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print 'Done seeding'
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grids = count / 8192
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pfxs = np.zeros((grids + 1, 256), dtype=np.int32)
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dpfxs = cuda.to_device(pfxs)
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launch('prefix_scan_8_0_shmem_shortseg', ddata, dpfxs,
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block=(32, 16, 1), grid=(grids, 1), stream=stream, l1=1)
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#dsplit = cuda.to_device(pfxs)
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#launch('crappy_split', dpfxs, dsplit,
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#block=(32, 8, 1), grid=(grids / 256, 1), stream=stream, l1=1)
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dsplit = cuda.mem_alloc(grids * 256 * 4)
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launch('better_split', dsplit, dpfxs,
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block=(32, 1, 1), grid=(grids / 32, 1), stream=stream)
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#if not stream:
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#split = cuda.from_device_like(dsplit, pfxs)
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#split_ = cuda.from_device_like(dsplit_, pfxs)
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#print np.all(split == split_)
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dshortseg_pfxs = cuda.mem_alloc(256 * 4)
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dshortseg_sums = cuda.mem_alloc(256 * 4)
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launch('prefix_sum', dpfxs, np.int32(grids * 256),
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dshortseg_pfxs, dshortseg_sums,
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block=(32, 8, 1), grid=(1, 1), stream=stream, l1=1)
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dsorted = cuda.mem_alloc(count * 4)
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launch('sort_8', ddata, dsorted, dpfxs,
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block=(32, 16, 1), grid=(grids, 1), stream=stream, l1=1)
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launch('sort_8_a', ddata, dsorted, dpfxs, dsplit,
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block=(32, 32, 1), grid=(grids, 1), stream=stream)
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if not stream:
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sorted = cuda.from_device(dsorted, (count,), np.int32)
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f = lambda r: ''.join(['\n\t%3d %4d %4d' % v for v in r])
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sort_stat = f(rle(sorted))
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with open('dev.txt', 'w') as fp: fp.write(sort_stat)
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sorted_np = np.sort(data)
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np_stat = f(rle(sorted_np))
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with open('cpu.txt', 'w') as fp: fp.write(np_stat)
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print 'is_sorted?', np.all(sorted == sorted_np)
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#data = np.fromstring(np.random.bytes(scale*block), dtype=np.uint16)
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def main():
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# shmem is known good; disable the CPU run to get better info from cuprof
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#go(8, 512<<10, True)
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#go(1024, 512<<8, False)
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#go(32768, 8192, False)
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stream = cuda.Stream() if '-s' in sys.argv else None
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go_sort(128<<20, stream)
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if stream:
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stream.synchronize()
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main()
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