{"title":"G-TSC: Timestamp Based Coherence for GPUs","authors":"Abdulaziz Tabbakh, Xuehai Qian, M. Annavaram","doi":"10.1109/HPCA.2018.00042","DOIUrl":null,"url":null,"abstract":"Cache coherence has been studied extensively in the context of chip multiprocessors (CMP). It is well known that conventional directory-based and snooping coherence protocols generate considerable coherence traffic as the number of hardware thread contexts increase. Since GPUs support hundreds or even thousands of threads, conventional coherence mechanisms when applied to GPUs will exacerbate the the bandwidth constraints that GPUs already face. Recognizing this constraint, prior work has proposed time-based coherence protocols. The main idea is to assign a lease period to the accessed cache block, and after the lease expires the cache block is self-invalidated. However, time-based coherence protocols require global synchronized clocks. Furthermore, this approach may increase execution stalls since threads have to wait to access data with an unexpired lease. Tardis is timestamp-based coherence protocol that has been proposed recently to alleviate the need for global clocks in CPUs. This paper builds on this prior work and proposes G-TSC, a novel cache coherence protocol for GPUs that is based on timestamp ordering. G-TSC conducts its coherence transactions in logical time. This work demonstrates the challenges in adopting timestamp coherence for GPUs which support massive thread parallelism and have unique microarchitecture features. This work then presents a number of solutions that tackle GPU-centric challenges. Evaluation of G-TSC implemented in the GPGPU-Sim simulation framework shows that G-TSC outperforms time-based coherence by 38% with release consistency.","PeriodicalId":154694,"journal":{"name":"2018 IEEE International Symposium on High Performance Computer Architecture (HPCA)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE International Symposium on High Performance Computer Architecture (HPCA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HPCA.2018.00042","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14
Abstract
Cache coherence has been studied extensively in the context of chip multiprocessors (CMP). It is well known that conventional directory-based and snooping coherence protocols generate considerable coherence traffic as the number of hardware thread contexts increase. Since GPUs support hundreds or even thousands of threads, conventional coherence mechanisms when applied to GPUs will exacerbate the the bandwidth constraints that GPUs already face. Recognizing this constraint, prior work has proposed time-based coherence protocols. The main idea is to assign a lease period to the accessed cache block, and after the lease expires the cache block is self-invalidated. However, time-based coherence protocols require global synchronized clocks. Furthermore, this approach may increase execution stalls since threads have to wait to access data with an unexpired lease. Tardis is timestamp-based coherence protocol that has been proposed recently to alleviate the need for global clocks in CPUs. This paper builds on this prior work and proposes G-TSC, a novel cache coherence protocol for GPUs that is based on timestamp ordering. G-TSC conducts its coherence transactions in logical time. This work demonstrates the challenges in adopting timestamp coherence for GPUs which support massive thread parallelism and have unique microarchitecture features. This work then presents a number of solutions that tackle GPU-centric challenges. Evaluation of G-TSC implemented in the GPGPU-Sim simulation framework shows that G-TSC outperforms time-based coherence by 38% with release consistency.