- 24 February 2022
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Parallel and Distributed Systems
- Vol. 33 (12) , 3415-3429
Abstract
Graphics Processing Units (GPUs) employ a shared memory, a software-managed cache for programmers, in each streaming multiprocessor to accelerate data sharing among the threads in a thread block. Although 60% of the shared memory space is underutilized, on average, there are some workloads that demand higher shared memory capacities. Therefore, improving shared memory utilization while satisfying the needs of shared memory intensive workloads is challenging. We make a key observation that the lifetime of each shared memory address is significantly shorter than the execution time of a thread block. In this paper, we first propose Off-Chip Shared Memory (OSM) that allocates shared memory space in the off-chip memory, and accelerates accesses to it via a small on-chip cache. Using an 8 KB cache for shared memory addresses, OSM provides almost the same performance as the baseline GPU that uses 96 KB on-chip shared memory. OSM improves GPU performance in two ways. First, it allocates higher shared memory capacities in the off-chip memory, and improves thread-level parallelism (TLP). Second, it designs a unified cache for shared memory and global address spaces, providing more caching space for global memory address space even for the workloads with high shared memory utilization. Our experimental results show an average 21% and 18% IPC improvement compared to the baseline and the state-of-the-art architectures.Keywords
Funding Information
- Iran National Science Foundation
This publication has 64 references indexed in Scilit:
- Simultaneous Multikernel: Fine-Grained Sharing of GPUsIEEE Computer Architecture Letters, 2015
- Cluster‐based approach for improving graphics processing unit performance by inter streaming multiprocessors localityIET Computers & Digital Techniques, 2015
- Stash2015
- Locality-Driven Dynamic GPU Cache Bypassing2015
- Power-efficient prefetching on GPGPUsThe Journal of Supercomputing, 2014
- Understanding the tradeoffs between software-managed vs. hardware-managed caches in GPUs2014
- Divergence-aware warp scheduling2013
- GPUWattch2013
- Orchestrated scheduling and prefetching for GPGPUs2013
- Cache-Conscious Wavefront Scheduling2012