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Hello OpenCL FPGA developers,
I have an OpenCL NDRange (64,1,1) kernel with 8 times replicated multiple local memories which make this kernel memory size limited (>100% M20s on A10). I have attempted to limit the replication factor by applying the newly introduced attribute in UG-OCL002 | 2020.04.13 20.1 aocl_programming_guide.pdf page 41.
Example for one of the buffers:
__local float __attribute__((private_copies(4))) x[M][N];
However, this attribute does not seem to have intended effect and I am stuck with 8 times replicate private copies. I know that reducing replication by a factor of 2 will make my kernel slower, but I could use less memory tradeoff for a bit slower kernel. Moreover, the speed decrease when all these buffers are used is a small percentage of the overall kernel schedule.
Thank you for your input.
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Can you post a snippet of the report for that specific local memory buffer before and after adding the pragma? Note that if the replication is happening to support "simultaneous work-groups", it won't be possible to reduce the replication factor using the "private_copies()" pragma or any other local memory pragma.
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Hi HRZ,
As I mentioned, the report shows before and after 8 copies. The same factor is for the loop of 4 where there is no need to replicate beyond 4. I suspect I have something wrong with the syntax of this attribute. Please note this is new attribute in 20.1 and a similar attribute exists in oneAPI, so you may have not seen it or tried it yet.
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Hi
as you can see from the below link
https://www.intel.com/content/www/us/en/programmable/documentation/mwh1391807965224.html
The private_copies attribute is useful when the scope of a variable (through its declaration or access pattern) is limited to a loop.
So if you can provide a code snippet and report that would help to debug the issue.
Thanks and Regards
Anil
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Hi Anil,
Thank you for pointing out the scope of this new attribute. However, our use case is a __local buffer copied into FPGA accelerator block memory in one loop to avoid paying the latency penalty for each of the accesses/copies from global memory. The __local buffer values are used in another loop for computation. This is a very typical use case scenario for FPGAs.
It appears that AOCL offline compiler builds a pipeline with a memory replication factor based on some default parameters assuming multiple workgroups accessing local buffer(s) without knowing apriori how many workgroups will be launched from global_size/local_zise ratio. The user does not have full control over the generated memory replication factor in this very typical use case. This results in wasted local memory resources by several factors. I would suggest extending the scope of the private_copies attribute to the whole kernel on a per __local buffer basis in the next release of the OpenCL compiler to give users full control over __local buffers replication factor.
The current remedy is to increase the reqd_work_group_size or max_work_goup_size which would indirectly change default AOCL compiler computation in favor of a smaller memory replication factors.
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Hi ,
Thanks for your valuable suggestions.
This has been noted and we are working on the same about providing more detailed explanations in the user guide about this attribute.
Thanks and Regards
Anil
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@AnilErinch_A_Intel Providing better explanation in the documentation is useful; however, it does not solve the underlying problem. What is required here is to give the users an extra attribute to control the on-chip memory replication factor for "supporting simultaneous work-groups". i.e. the possibility to control the number of work-groups the compiler would schedule simultaneously in a single compute unit. The functionality already exists in the compiler; it just needs to be exposed to the users in form of an attribute/pragma. I opened a support ticket with Altera/Intel about this exact problem years ago, but such attribute/pragma is yet to be provided.
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Hi @HRZ,
Thanks for the suggestions ,
I will check with the team , about the feasibility of exposing this feature.
Thanks and Regards
Anil
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