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Shixiong Xu, David Gregg
Memory performance is of great importance to achieve high performance on the Nvidia CUDA GPU. Previous work has proposed specific optimizations such as thread coarsening, caching data in shared memory, and global data layout transformation. We argue that vectorization based on hyper loop parallelism can be used as a unified technique to optimize the memory […]
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Nadir Gamal Abdelrahim Salih
Heterogeneous systems are computer systems that exploit multiple devices with different processor architectures to improve the computing efficiency by offloading workloads to the device that fits them best. OpenCL is a framework for building portable applications that run across different devices in heterogeneous systems. It has gained traction as a powerful tool for high-performance computing. […]
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John Wickerson
Despite the conceptual simplicity of sequential consistency (SC), the semantics of SC atomic operations and fences in the C11 and OpenCL memory models is subtle, leading to convoluted prose descriptions that translate to complex axiomatic formalisations. We conduct an overhaul of SC atomics in C11, reducing the associated axioms in both number and complexity. A […]
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Kazuhiko Ohno, Tomoharu Kamiya, Takanori Maruyama, Masaki Matsumoto
Although General Purpose computation on Graphics Processing Units (GPGPU) is widely used for the high-performance computing, standard programming frameworks such as CUDA and OpenCL are still difficult to use.They require low-level specifications and the hand-optimization is a large burden. Therefore we are developing an easier framework named MESI-CUDA. Based on a virtual shared memory model, […]
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John Wickerson, Mark Batty
We study how the C11 memory model can be simplified and how it can be extended. Our first contribution is to propose a mild strengthening of the model that enables the rules pertaining to sequentially-consistent (SC) operations to be significantly simplified. We eliminate one of the total orders that candidate executions must range over, leading […]
Koji Nakano
The Hierarchical Memory Machine (HMM) is a theoretical parallel computing model that captures the essence of architecture of CUDA-enabled GPUs. The main contribution of this paper is to present an efficient implementation of the O(n^3)-time dynamic programming algorithm for solving the optimal triangulation problem for a convex n-gon in the HMM. Although the HMM can […]
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Sparsh Mittal; Jeffrey S. Vetter; Dong Li
Recent trends of CMOS scaling and increasing number of on-chip cores have led to a large increase in the size of on-chip caches. Since SRAM has low density and consumes large amount of leakage power, its use in designing on-chip caches has become more challenging. To address this issue, researchers are exploring the use of […]
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Tyler Rey Sorensen
Graphics Processing Units (GPUs) are highly parallel shared memory microprocessors, and as such, they are prone to the same concurrency considerations as their traditional multicore CPU counterparts. In this thesis, we consider shared memory consistency, i.e. what values can be read when issued concurrently with writes on current GPU hardware. While memory consistency has been […]
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Koji Nakano, Susumu Matsumae, Yasuaki Ito
The Discrete Memory Machine (DMM) is a theoretical parallel computing model that captures the essence of memory access to the shared memory of a streaming multiprocessor on CUDA-enabled GPUs. The DMM has w memory banks that constitute a shared memory, and w threads in a warp try to access them at the same time. However, […]
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Bin Ren
SIMD accelerators and many-core coprocessors with coarse-grained and fine-grained level parallelism, become more and more popular. Streaming SIMD Extensions (SSE), Graphics Processing Unit (GPU), and Intel Xeon Phi (MIC) can provide orders of magnitude better performance and efficiency for parallel workloads as compared to single core CPUs. However, parallelizing irregular applications involving dynamic data structures […]
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John Cheng, Max Grossman, Ty McKercher
Designed for professionals across multiple industrial sectors, Professional CUDA C Programming presents CUDA — a parallel computing platform and programming model designed to ease the development of GPU programming — fundamentals in an easy-to-follow format, and teaches readers how to think in parallel and implement parallel algorithms on GPUs. Each chapter covers a specific topic, […]
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Raphael Landaverde, Tiansheng Zhang, Ayse K. Coskun, Martin Herbordt
Managing memory between the CPU and GPU is a major challenge in GPU computing. A programming model, Unified Memory Access (UMA), has been recently introduced by Nvidia to simplify the complexities of memory management while claiming good overall performance. In this paper, we investigate this programming model and evaluate its performance and programming model simplifications […]
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