A multiple SIMD, multiple data (MSMD) architecture: Parallel execution of dynamic and static SIMD fragments

Wang, Yaohua; Chen, Shuming; Wan, Jiguang; Meng, Jiayuan; Zhang, Kai; Liu, Wei; Ning, Xi

doi:10.1109/hpca.2013.6522353

Cited by 10 publications

(2 citation statements)

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“…This approach supports very restricted cases and fails if the locking happens in diverged code. Multiple SIMD Multiple Data (MSMD) [234] proposed flexible SIMD datapaths to handle divergence through repartitioning those datapaths among multiple control-flow paths.…”

Section: ) Hardware Supported Modifications (Hardware Solution)mentioning

confidence: 99%

“…Branch prediction [81], [157], [190] Instruction fetch [157] Automatic codes Generation on SIMD units [115], [157] Data\code partitioning across the multiple processor elements [234] Automatic Simdization techniques [190], [248]- [253] General directivebased models directive-based GPU programming models [84], [96], [98], [109], [116]- [120] continued on next page…”

Section: Compiler Optimization Techniquesmentioning

confidence: 99%

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MIMD Programs Execution Support on SIMD Machines: A Holistic Survey

Mustafa,

Alkhasawneh,

Obeidat

et al. 2024

IEEE Access

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The Single Instruction Multiple Data (SIMD) architecture, supported by various highperformance computing platforms, efficiently utilizes data-level parallelism. The SIMD model is used in traditional CPUs, dedicated vector systems, and accelerators such as GPUs, vector extensions, and Xeon Phi. It provides performance throughput in computation-intensive and data-parallel applications. Despite the similarity of data-processing principles between these architectures, porting various programming models between the reviewed platforms is challenging. Furthermore, enhancing the programmability of these architectures is an important feature for utilizing their emerging computing power and simplifying programming complexity. This paper reviews the basic principles of optimization techniques to run asynchronous Multiple Instruction Multiple Data (MIMD) on SIMD accelerators. It also surveys several GPU programming paradigms and application programming interfaces (APIs) and classifies these frameworks into different groups based on their criteria. In addition, a review of studies that performed a comparison of the collaborative execution of GPUs with CPUs and Xeon Phi is presented in this paper. This study will be beneficial for developers and researchers in the field of computer architecture and parallel computing of intensive scientific applications, specifically for early-stage high-performance computing researchers, to obtain a brief overview of performance optimization opportunities as well as the challenges of existing SIMD platforms.

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Section: ) Hardware Supported Modifications (Hardware Solution)mentioning

confidence: 99%

Section: Compiler Optimization Techniquesmentioning

confidence: 99%

MIMD Programs Execution Support on SIMD Machines: A Holistic Survey

Mustafa,

Alkhasawneh,

Obeidat

et al. 2024

IEEE Access

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Performance Analysis of Existing SIMD Architectures

Cui¹,

Zhou

Jin

2019

Communications in Computer and Information Science

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A scalable multi-path microarchitecture for efficient GPU control flow

ElTantawy¹,

Wenjie²,

O'Connor

et al. 2014

2014 IEEE 20th International Symposium on High Performance Computer Architecture (HPCA)

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Graphics processing units (GPUs) are increasingly used for non-graphics computing. However, applications with divergent control flow incur performance degradation on current GPUs. These GPUs implement the SIMT execution model by serializing the execution of different control flow paths encountered by a warp. This serialization can mask thread level parallelism among the scalar threads comprising a warp thus degrading performance. In this paper, we propose a novel branch divergence handling mechanism that enables interleaved execution of divergent paths within a warp while maintaining immediate postdominator reconvergence. This multi-path microarchitecture decouples divergence and reconvergence tracking by replacing the stack-based structure typically employed to support SIMT execution with two tables: a warp split table and a warp reconvergence table. It also enables reconvergence before the immediate postdominator which is important for efficient execution of unstructured control flow. Evaluated on a set of benchmarks with complex divergent control flow, our proposal achieves up to a 7× speedup with a harmonic mean of 32% over conventional single-path SIMT execution.

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A multiple SIMD, multiple data (MSMD) architecture: Parallel execution of dynamic and static SIMD fragments

Cited by 10 publications

References 20 publications

MIMD Programs Execution Support on SIMD Machines: A Holistic Survey

MIMD Programs Execution Support on SIMD Machines: A Holistic Survey

Performance Analysis of Existing SIMD Architectures

A scalable multi-path microarchitecture for efficient GPU control flow

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