Created
September 4, 2013 12:40
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GPU Torch Benchmark
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-- Max Jaderberg 4/9/13 | |
-- GPU Effectiveness test | |
require 'torch' | |
require 'sys' | |
require 'nn' | |
require 'xlua' | |
cmd = torch.CmdLine() | |
cmd:text() | |
cmd:text('GPU Benchmark. Max Jaderberg.') | |
cmd:text() | |
cmd:text('Options:') | |
cmd:option('-size', 24, 'size of images') | |
cmd:option('-N', 1000, 'number of images') | |
cmd:option('-threads', 2, 'number of threads') | |
cmd:text() | |
opt = cmd:parse(arg or {}) | |
torch.setnumthreads(opt.threads) | |
torch.setdefaulttensortype("torch.FloatTensor") | |
-- Input size | |
sz = opt.size | |
finalsize = (sz - 4)/2 --for a 5x5 filter | |
Ntest = opt.N | |
-- Test input | |
x = torch.rand(Ntest, 3, sz, sz) | |
-- Simple network | |
model = nn.Sequential() | |
model:add(nn.SpatialConvolution(3, 64, 5, 5)) | |
model:add(nn.Tanh()) | |
model:add(nn.SpatialLPPooling(64, 2, 2, 2, 2, 2)) | |
model:add(nn.Reshape(64*finalsize*finalsize)) | |
model:add(nn.Linear(64*finalsize*finalsize, 128)) | |
model:add(nn.Tanh()) | |
model:add(nn.Linear(128, 2)) | |
model:add(nn.Tanh()) | |
criterion = nn.MSECriterion() | |
-- Test ouput | |
y = torch.rand(Ntest, 2) | |
-- Test routine | |
local runtest = function() | |
print('==> Type is '..x:type()) | |
for i = 1,x:size(1) do | |
xlua.progress(i, x:size(1)) | |
local yp = model:forward(x[i]) | |
local err = criterion:forward(yp, y[i]) | |
local df_do = criterion:backward(yp, y[i]) | |
model:backward(x[i], df_do) | |
end | |
end | |
-- CPU TEST | |
cputime0 = sys.clock() | |
runtest() | |
cputime1 = sys.clock() | |
cputime = cputime1 - cputime0 | |
print('CPU Time: '.. (cputime*1000) .. 'ms') | |
-- GPU TEST | |
require 'cunn' | |
x = x:cuda() | |
y = y:cuda() | |
model:cuda() | |
criterion:cuda() | |
gputime0 = sys.clock() | |
runtest() | |
gputime1 = sys.clock() | |
gputime = gputime1 - gputime0 | |
print('GPU Time: '.. (gputime*1000) .. 'ms') | |
print('------------------') | |
print('GPU speedup: '..cputime/gputime..'x') | |
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