Towards Application-Centric Parallel Memories.

Lecture Notes in Computer Science(2018)

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Many applications running on parallel processors and accelerators are bandwidth bound. In this work, we explore the benefits of parallel (scratch-pad) memories to further accelerate such applications. To this end, we propose a comprehensive approach to designing and implementing application-centric parallel memories based on the polymorphic memory-model called PolyMem. Our approach enables the acceleration of a memory-bound region of an application by (1) analyzing the memory access to extract parallel accesses, (2) configuring PolyMem to deliver maximum speed-up for the detected accesses, and (3) building an actual FPGA-based parallel-memory accelerator for this region, with predictable performance. We validate our approach on 10 instances of Sparse-STREAM (a STREAM benchmark adaptation with sparse memory accesses), for which we design and benchmark the corresponding parallel-memory accelerators in hardware. Our results demonstrate that building parallel-memory accelerators is feasible and leads to performance gain, but their efficient integration in heterogeneous platforms remains a challenge.
Polymorphic parallel memory,Memory bandwidth improvement,Parallel-memory accelerator
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