You're right that something is wrong. There's no way to justify 0.2
seconds per iteration of overhead.
Could you confirm that you are not doing any buffer copy in the meantime?
Did you retry with a different OpenCL Platform (e.g. AMD CPU or Intel CPU)?
What happens if you pipeline the kernel execution?
events = []
kernel_total = 0
t0 = time.time()
for i in range(64):
events.append(prog.sha1( queue , shape , None , in_buf , out_buf ,
..<other buffers> ))
t2 = time.time()
print("Scheduling time: %f", t2 - t0)
t1 = t2
for event in events:
event.wait()
t2 = time.time()
kernel_elapsed = 1e-9 * ( event.profile.end - event.profile.start )
kernel_total += kernel_elapsed
print("Real run time: %f, Kernel time: %f", t2 - t1, kernel_elapsed)
t1 = t2
print("Total real run time: %f, Total kernel time: %f", t2 - t0,
kernel_total)
On 11 June 2014 23:15, Abhilash Dighe <[email protected]> wrote:
> Hi,
>
> I was hoping to get some insight on my observations. I am using PyOpenCL
> version 2 with NVIDIA Tesla M2090 to run my kernel which runs SHA1
> algorithm over variably sized data blocks. I'm running the same kernel I'm
> trying to find the execution time for my kernel. But I'm getting different
> readings for time for when I use the PyOpenCL's profiling tool and when I
> use the standard python time library. My code is structured as:
>
>
> hash_start = time.time()
> hash_event = prog.sha1( queue , shape , None , in_buf , out_buf , ..<other
> buffers> )
> hash_event.wait()
> hash_end = time.time()
> add_hash_CPU_time( hash_end - hash_start )
> add_hash_GPU_time( 1e-9 * ( hash_event.profile.end -
> hash_event.profile.start ) )
>
> These are the results for a test case of size 3 GB. The kernel gets called
> 64 times and runs 12288 threads each time.
>
> Total OpenCL profiling time = 1.56s
> Total CPU wall clock time = 13.79s
>
> I needed some help understanding what the cause for this inconsistency is.
> Or is there any mistake I'm making in recording the data.
>
> Regards,
> Abhilash Dighe
>
> _______________________________________________
> PyOpenCL mailing list
> [email protected]
> http://lists.tiker.net/listinfo/pyopencl
>
>
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