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Optimizing Memory Efficiency For Man...

Li, Chao.

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Optimizing Memory Efficiency For Many-Core Architecture.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Optimizing Memory Efficiency For Many-Core Architecture./
作者:	Li, Chao.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
面頁冊數:	158 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
Contained By:	Dissertation Abstracts International78-08B(E).
標題:	Computer engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10583457
ISBN:	9781369621365

Optimizing Memory Efficiency For Many-Core Architecture.
Li, Chao.

Optimizing Memory Efficiency For Many-Core Architecture. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 158 p.

Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.

Thesis (Ph.D.)--North Carolina State University, 2016.

Massively parallel, throughput-oriented processors such as graphics processing units (GPUs) leverage high thread-level parallelism to overlap long latency memory accesses with computation. On-chip memory resources, including register files, shared memory, and data caches which were designed to provide high-bandwidth low-latency data accesses, remain critical to application performance.

ISBN: 9781369621365Subjects--Topical Terms:

621879
Computer engineering.

Optimizing Memory Efficiency For Many-Core Architecture.
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500 $a Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
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520 $a Massively parallel, throughput-oriented processors such as graphics processing units (GPUs) leverage high thread-level parallelism to overlap long latency memory accesses with computation. On-chip memory resources, including register files, shared memory, and data caches which were designed to provide high-bandwidth low-latency data accesses, remain critical to application performance.
520 $a In this dissertation, we study these memory resources and propose optimizations to improve the memory efficiency for GPU architecture in a whole stack, from hardware architecture, compiler, to application-level algorithms. First, I will present our work on understanding the tradeoffs between software-managed vs. hardware-managed caches in GPUs. To manage on-chip caches, either software-managed or hardware-managed schemes can be employed. State-of-the-art accelerators, such as the NVIDIA Fermi or Kepler GPUs support both software-managed caches, aka, shared memory, and hardware-managed L1 data caches (D-caches). Shared memory and the L1 D-cache on a GPU utilize the same physical storage and their capacity can be configured at runtime. In this work, we present an in-depth study to reveal interesting and sometimes unexpected tradeoffs between shared memory and the hardware-managed L1 D-caches in GPU architecture.
520 $a Secondly, I will present our novel compiler scheme for the judicious utilization of onchip memory resources on GPUs. To manage these intricate on-chip memory resources is nontrivial for application developers. Moreover, the varying on-chip resource across different GPU generations makes performance portability a daunting challenge. In this study, we propose compiler-driven automatic data placement scheme, to refine GPU programs by altering data placement among different on-chip resources to achieve both performance enhancement and performance portability.
520 $a Thirdly, I will present our study on novel cache optimization for GPU architecture. Onchip L1 D-caches are critical resources for providing high-bandwidth and low-latency data accesses. We observe that the memory access streams to L1 D-caches for many applications contain a significant amount of requests with low reuse, which greatly reduce the cache efficacy. We propose an efficient locality monitoring mechanism to dynamically filter the access stream on cache insertion such that only the data with high reuse and short reuse distances are stored in the L1 D-cache. We propose a design that integrates the locality filtering functionality into the decoupled tag store of the current L1 D-cache through simple and cost effective hardware extensions.
520 $a Finally, I will present our study on algorithm-level memory efficiency optimization for GPUs. We optimize the memory efficiency for accelerating deep Convolutional Neural Networks (CNNs), the state-of-the-art machine learning algorithm, on GPUs. Existing works on GPU-accelerated deep CNNs mainly focus on the computational efficiency of CNNs while the memory efficiency of CNNs have been largely overlooked. In this study, we look into the memory efficiency of various CNN layers on GPUs and reveal the performance implications from both data layouts and memory access patterns. Then we propose a set of methods to optimize memory efficiency for accelerating CNNs on GPUs. The experiment results demonstrate the effectiveness of our memory optimizations and their universal effects on different types of layers and various complete networks.
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