コンピューターの処理速度を2倍にする方法が発見される(Method identified to double computer processing speeds)

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2024-02-21 カリフォルニア大学リバーサイド校(UCR)

UC Riversideのツェン・ハングウェイ准教授は、「Simultaneous and Heterogeneous Multithreading」という論文で、既存のハードウェアを使用してスマートフォン、タブレット、パソコン、サーバーの処理能力を倍増させるコンピュータアーキテクチャの新しいパラダイムを提案しました。この新手法は、複数のコアARMプロセッサー、NVIDIA GPU、Tensor Processing Unitハードウェアアクセラレータを同時に活用し、1.96倍の速度向上とエネルギー消費量の51%削減を達成しました。

<関連情報>

同時かつ異種マルチスレッド Simultaneous and Heterogenous Multithreading

Kuan-Chieh Hsu,Hung-Wei Tseng
MICRO ’23: Proceedings of the 56th Annual IEEE/ACM International Symposium on Microarchitecture  Published:08 December 2023
DOI:https://doi.org/10.1145/3613424.3614285

ABSTRACT

The landscape of modern computers is undoubtedly heterogeneous, as all computing platforms integrate multiple types of processing units and hardware accelerators. However, the entrenched programming models focus on using only the most efficient processing units for each code region, underutilizing the processing power within heterogeneous computers.

This paper simultaneous and heterogenous multithreading (SHMT), a programming and execution model that enables opportunities for “real” parallel processing using heterogeneous processing units. In contrast to conventional models, SHMT can utilize heterogeneous types of processing units concurrently for the same code region. Furthermore, SHMT presents an abstraction and a runtime system to facilitate parallel execution. More importantly, SHMT needs to additionally address the heterogeneity in data precision that various processing units support to ensure the quality of the result.

This paper implements and evaluates SHMT on an embedded system platform with a GPU and an Edge TPU. SHMT achieves up to 1.95 × speedup and  51.0% energy reduction compared to GPU baseline.

1601コンピュータ工学
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