Scratchpad Sharing in GPUs

TL;DR

This paper introduces Scratchpad Sharing, a combination of architectural and compiler optimizations for GPUs that enhances scratchpad memory utilization, leading to significant performance improvements in GPGPU applications.

Contribution

It proposes a novel scratchpad sharing technique with scheduling and compiler strategies to better utilize scratchpad memory in GPUs.

Findings

Average performance improvement of 19% across tested kernels

Maximum improvement of 92.17% in certain kernels

Effective utilization of unutilized scratchpad memory

Abstract

GPGPU applications exploit on-chip scratchpad memory available in the Graphics Processing Units (GPUs) to improve performance. The amount of thread level parallelism present in the GPU is limited by the number of resident threads, which in turn depends on the availability of scratchpad memory in its streaming multiprocessor (SM). Since the scratchpad memory is allocated at thread block granularity, part of the memory may remain unutilized. In this paper, we propose architectural and compiler optimizations to improve the scratchpad utilization. Our approach, Scratchpad Sharing, addresses scratchpad under-utilization by launching additional thread blocks in each SM. These thread blocks use unutilized scratchpad and also share scratchpad with other resident blocks. To improve the performance of scratchpad sharing, we propose Owner Warp First (OWF) scheduling that schedules warps from the additional thread blocks effectively. The performance of this approach, however, is limited by the availability of the shared part of scratchpad. We propose compiler optimizations to improve the availability of shared scratchpad. We describe a scratchpad allocation scheme that helps in allocating scratchpad variables such that shared scratchpad is accessed for short duration. We introduce a new instruction, relssp, that when executed, releases the shared scratchpad. Finally, we describe an analysis for optimal placement of relssp instructions such that shared scratchpad is released as early as possible. We implemented the hardware changes using the GPGPU-Sim simulator and implemented the compiler optimizations in Ocelot framework. We evaluated the effectiveness of our approach on 19 kernels from 3 benchmarks suites: CUDA-SDK, GPGPU-Sim, and Rodinia. The kernels that underutilize scratchpad memory show an average improvement of 19% and maximum improvement of 92.17% compared to the baseline approach.