A Case for Scoped Persist Barriers in GPUs
| Autor: | Mitesh R. Meswani, Arkaprava Basu, Sooraj Puthoor, Dibakar Gope |
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| Rok vydání: | 2018 |
| Předmět: |
010302 applied physics
Scope (project management) Computer science Distributed computing 02 engineering and technology computer.software_genre 01 natural sciences 020202 computer hardware & architecture Software framework 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Key (cryptography) Non-volatile random-access memory Isolation (database systems) Persistent data structure computer |
| Zdroj: | GPGPU@PPoPP |
| Popis: | Two key trends in computing are evident --- emergence of GPU as a first-class compute element and emergence of byte-addressable nonvolatile memory technologies (NVRAM) as DRAM-supplement. GPUs and NVRAMs are likely to coexist in future systems. However, previous works have either focused on GPUs or on NVRAMs, in isolation. In this work, we investigate the enhancements necessary for a GPU to efficiently and correctly manipulate NVRAM-resident persistent data structures.Specifically, we find that previously proposed CPU-centric persist barriers fall short for GPUs. We thus introduce the concept of scoped persist barriers that aligns with the hierarchical programming framework of GPUs. Scoped persist barriers enable GPU programmers to express which execution group (a.k.a., scope) a given persist barrier applies to. We demonstrate that: 1 use of narrower scope than algorithmically-required can lead to inconsistency of persistent data structure, and 2 use of wider scope than necessary leads to significant performance loss (e.g., 25% or more). Therefore, a future GPU can benefit from persist barriers with different scopes. |
| Databáze: | OpenAIRE |
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