Memory issue #42
Comments
|
Can you show the output of
GetOpenCLInfo
and the full output of your call to RandomiseGroupLevel ?
2017-11-13 21:21 GMT+01:00 cmehta126 <[email protected]>:
… I'm running Broccoli for permutation tests on MRI data. I'm getting an
error of the type:
Run kernel error for kernel 'CalculateBetaWeightsGLM' is
'CL_MEM_OBJECT_ALLOCATION_FAILURE'
Run kernel error for kernel 'CalculateStatisticalMapsGLMTTest' is
'CL_MEM_OBJECT_ALLOCATION_FAILURE'
Run kernel error for kernel 'CalculateStatisticalMapsGLMTTestSecondLevelPermutation'
is 'CL_MEM_OBJECT_ALLOCATION_FAILURE'
Run kernel error for kernel 'TransformData' is 'CL_MEM_OBJECT_ALLOCATION_
FAILURE'
It seems this is a memory issue. Are there anyways getting around this?
The data I'm permuting are spatial maps of dimension 256x256x256 for
several hundred samples. I'm using a mask generated from "Smoothing".
What is more is that, prior to this error, the output of
RandomiseGroupLevel states
Permutation threshold for contrast 1 for a significance level of 0.050000
is 0.000000
Permutation threshold for contrast 2 for a significance level of 0.050000
is 0.000000
Permutation threshold for contrast 3 for a significance level of 0.050000
is 0.000000
Could this imply that the data is too highly correlated over voxels for
"RandomiseGroupLevel" to work properly or is it most likely a memory issue?
thank you.
Best,
Chintan
—
You are receiving this because you are subscribed to this thread.
Reply to this email directly, view it on GitHub
<#42>, or mute the thread
<https://github.com/notifications/unsubscribe-auth/AEGryE4M0tzK714xLjGjD_T80UmrKgpJks5s2KTUgaJpZM4QcWuo>
.
--
Anders Eklund, PhD
|
|
RandomiseGroupLevel worked as I expected on my dataset after downsampling spatial maps in the input volume from 256x256x256 (1mm x 1mm x 1mm) to 128x128x128 (2mm x 2mm x 2mm). That significantly reduced the amount of memory needed for loading this data, without sacrificing specificity. The voxel resolution of the original data from diffusion weighted imaging (DWI) was on the order of (2mm x 2mm x 2mm) to begin with. I registered the DWI to FreeSurfer's CVS template which has voxel resolution (1mm x 1mm x 1mm) to enable group analysis, but I don't believe there is much loss of information by downsampling. Regardless, here is the output from GetOpenCLInfo and RandomiseGroupLevel when using volumes of spatial maps with dimesnionality 256x256x256 (as I did originally and got error with) GetOpenCLInfo:
The output of the call to RandomiseGroupLevel (this here is with 500 permutations. But same thing held when running 5000 permutations).
|
|
362 volumes of size 256 x 256 x 256 requires about 22.6 GB of memory in
float format, while your graphics card has 11 GB of memory.
2017-11-14 0:26 GMT+01:00 cmehta126 <[email protected]>:
… RandomiseGroupLevel worked as I expected on my dataset after downsampling
spatial maps in the input volume from 256x256x256 (1mm x 1mm x 1mm) to
128x128x128 (2mm x 2mm x 2mm). That significantly reduced the amount of
memory needed for loading this data, without sacrificing specificity. The
voxel resolution of the original data from diffusion weighted imaging (DWI)
was on the order of (2mm x 2mm x 2mm) to begin with. I registered the DWI
to FreeSurfer's CVS template which has voxel resolution (1mm x 1mm x 1mm)
to enable group analysis, but I don't believe there is much loss of
information by downsampling.
Regardless, here is the output from GetOpenCLInfo and RandomiseGroupLevel
when using volumes of spatial maps with dimesnionality 256x256x256 (as I
did originally and got error with)
GetOpenCLInfo:
Device info
------------------------------
Platform number: 0 Platform vendor: NVIDIA Corporation
Platform name: NVIDIA CUDA
Platform extentions: cl_khr_global_int32_base_atomics
cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics
cl_khr_local_int32_extended_atomics cl_khr_fp64
cl_khr_byte_addressable_store cl_khr_icd cl_khr_gl_sharing
cl_nv_compiler_options cl_nv_device_attribute_query cl_nv_pragma_unroll
cl_nv_copy_opts cl_nv_create_buffer
Platform profile: FULL_PROFILE
------------------------------
Device number: 0
Device vendor: NVIDIA Corporation
Device name: Tesla K80
Hardware version: OpenCL 1.2 CUDA
Software version: 375.66
OpenCL C version: OpenCL C 1.2
Device extensions: cl_khr_global_int32_base_atomics
cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics
cl_khr_local_int32_extended_atomics cl_khr_fp64
cl_khr_byte_addressable_store cl_khr_icd cl_khr_gl_sharing
cl_nv_compiler_options cl_nv_device_attribute_query cl_nv_pragma_unroll
cl_nv_copy_opts cl_nv_create_buffer
Global memory size in MB: 11439
Size of largest memory object in MB: 2859
Global memory cache size in KB: 208
Local memory size in KB: 48
Constant memory size in KB: 64
Parallel compute units: 13
Clock frequency in MHz: 823
Max number of threads per block: 1024
Max number of threads in each dimension: 1024 1024 64
The output of the call to RandomiseGroupLevel (this here is with 500
permutations. But same thing held when running 5000 permutations).
Authored by K.A. Eklund
Data size: 256 x 256 x 256 x 362
Number of permutations: 500
Number of regressors: 8
Number of contrasts: 3
Performing 3 t-tests
Correlation design detected for t-contrast 1
Correlation design detected for t-contrast 2
Correlation design detected for t-contrast 3
Max number of permutations for contrast 1 is inf
Max number of permutations for contrast 2 is inf
Max number of permutations for contrast 3 is inf
Starting permutation 1
Starting permutation 101
Starting permutation 201
Starting permutation 301
Starting permutation 401
Permutation threshold for contrast 1 for a significance level of 0.050000
is 0.000000
Starting permutation 1
Starting permutation 101
Starting permutation 201
Starting permutation 301
Starting permutation 401
Permutation threshold for contrast 2 for a significance level of 0.050000
is 0.000000
Starting permutation 1
Starting permutation 101
Starting permutation 201
Starting permutation 301
Starting permutation 401
Permutation threshold for contrast 3 for a significance level of 0.050000
is 0.000000
Run kernel error for kernel 'CalculateBetaWeightsGLM' is
'CL_MEM_OBJECT_ALLOCATION_FAILURE'
Run kernel error for kernel 'CalculateStatisticalMapsGLMTTest' is
'CL_MEM_OBJECT_ALLOCATION_FAILURE'
Run kernel error for kernel 'CalculateStatisticalMapsGLMTTestSecondLevelPermutation'
is 'CL_MEM_OBJECT_ALLOCATION_FAILURE'
Run kernel error for kernel 'TransformData' is 'CL_MEM_OBJECT_ALLOCATION_
FAILURE'
—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub
<#42 (comment)>,
or mute the thread
<https://github.com/notifications/unsubscribe-auth/AEGryD0NQkDulCw5zSKLk0nuytV_1_PCks5s2NALgaJpZM4QcWuo>
.
--
Anders Eklund, PhD
|
|
Thank you. Does Broccoli have a way of using additional memory to augment the RAM of a graphics card, given my graphics card is limited to 11GB of memory (with largest object size capped at 2.8 GB). I have available ~1 TB of Fast SSD memory mounted. |
|
No, but you can install an OpenCL driver for the CPU, and then run BROCCOLI
in parallel on the CPU cores.
2017-11-14 17:23 GMT+01:00 cmehta126 <[email protected]>:
… Thank you. Does Broccoli have a way of using additional memory to augment
the RAM of a graphics card, given my graphics card is limited to 11GB of
memory (with largest object size capped at 2.8 GB). I have available ~1 TB
of Fast SSD memory mounted.
—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub
<#42 (comment)>,
or mute the thread
<https://github.com/notifications/unsubscribe-auth/AEGryKoYGScfPYI64WhSo2NXct3D_Oyqks5s2b5pgaJpZM4QcWuo>
.
--
Anders Eklund, PhD
|
|
Thank you, that is very helpful.
…On Nov 15, 2017 8:47 AM, "Anders Eklund" ***@***.***> wrote:
No, but you can install an OpenCL driver for the CPU, and then run BROCCOLI
in parallel on the CPU cores.
2017-11-14 17:23 GMT+01:00 cmehta126 ***@***.***>:
> Thank you. Does Broccoli have a way of using additional memory to augment
> the RAM of a graphics card, given my graphics card is limited to 11GB of
> memory (with largest object size capped at 2.8 GB). I have available ~1
TB
> of Fast SSD memory mounted.
>
> —
> You are receiving this because you commented.
> Reply to this email directly, view it on GitHub
> <#42 (comment)
>,
> or mute the thread
> <https://github.com/notifications/unsubscribe-auth/
AEGryKoYGScfPYI64WhSo2NXct3D_Oyqks5s2b5pgaJpZM4QcWuo>
> .
>
--
Anders Eklund, PhD
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub
<#42 (comment)>,
or mute the thread
<https://github.com/notifications/unsubscribe-auth/AE74rhAh1rN_bLHhmYaqlVQsiTydOuvrks5s2ut-gaJpZM4QcWuo>
.
|
and others
I'm running Broccoli for permutation tests on MRI data. I'm getting an error of the type:
It seems this is a memory issue. Are there anyways getting around this? The data I'm permuting are spatial maps of dimension 256x256x256 for several hundred samples. I'm using a mask generated from "Smoothing".
What is more is that, prior to this error, the output of RandomiseGroupLevel states
Could this imply that the data is too highly correlated over voxels for "RandomiseGroupLevel" to work properly or is it most likely a memory issue? The volumes file is 2.5 GB in total with 356 subjects. My device information is:
It seems the hardware could theoretically handle loading 2.5 Gigs of data (but not sure if it is enough for Permutation tests).
thank you.
Best,
Chintan
The text was updated successfully, but these errors were encountered: