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Optimizing Sparse Matrix-Vector Multiplication on Emerging Many-Core Architectures

Shizhao Chen, Jianbin Fang, Donglin Chen, Chuanfu Xu, Zheng Wang

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Tags: Computer science, Intel Xeon Phi, Machine learning, OpenMP, Performance, Sparse matrix

June 9, 2018 by hgpu

GPU Virtualization and Scheduling Methods: A Comprehensive Survey

Cheol-Ho Hong, Ivor Spence, Dimitrios S. Nikolopoulos

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Tags: Cloud, Computer science, CUDA, Distributed computing, Heterogeneous systems, nVidia, OpenCL, Review, Virtualization

June 9, 2018 by hgpu

A High-efficiency FPGA-based Accelerator for Convolutional Neural Networks using Winograd Algorithm

Y. Huang, J. Shen, Z. Wang, M. Wen, C. Zhang

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Tags: Computational Complexity, Computer science, Computer vision, Deep learning, FPGA, HLS, Neural networks

June 9, 2018 by hgpu

Fast Locality Sensitive Hashing for Beam Search on GPU

Xing Shi, Shizhen Xu, Kevin Knight

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, Data Structures and Algorithms, Hashing, NLP, nVidia, Tesla K20

June 9, 2018 by hgpu

clMF: A fine-grained and portable alternating least squares algorithm for parallel matrix factorization

Jing Chen, Jianbin Fanga, Weifeng Liub, Tao Tang, Canqun Yang

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Tags: AMD Radeon R9 Fury X, ATI, Computer science, Factorization, Linear Algebra, nVidia, OpenCL, Package, Tesla K20

June 2, 2018 by hgpu

Design of FPGA-Based Accelerator for Convolutional Neural Network under Heterogeneous Computing Framework with OpenCL

Li Luo, Yakun Wu, Fei Qiao, Yi Yang, Qi Wei, Xiaobo Zhou, Yongkai Fan, Shuzheng Xu, Xinjun Liu, Huazhong Yang

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Tags: Algorithms, CNN, Computer science, Deep learning, FPGA, Heterogeneous systems, Neural networks, OpenCL

June 2, 2018 by hgpu

NengoDL: Combining deep learning and neuromorphic modelling methods

Daniel Rasmussen

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Tags: Artificial intelligence, Benchmarking, Computer science, Deep learning, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package, Python

June 2, 2018 by hgpu

Marian: Cost-effective High-Quality Neural Machine Translation in C++

Marcin Junczys-Dowmunt, Kenneth Heafield, Hieu Hoang, Roman Grundkiewicz, Anthony Aue

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Tags: Computer science, CUDA, Machine learning, NLP, nVidia, Package

June 2, 2018 by hgpu

OpenCL 2.2 API Specification

Khronos OpenCL Working Group

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Tags: Computer science, OpenCL, Whitepaper

May 26, 2018 by hgpu

Transformations of High-Level Synthesis Codes for High-Performance Computing

Johannes de Fine Licht, Simon Meierhans, Torsten Hoefler

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Tags: Computer science, FPGA, HLS, OpenCL

May 26, 2018 by hgpu

Learning to Optimize Tensor Programs

Tianqi Chen, Lianmin Zheng, Eddie Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

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Tags: ARM, Computer science, CUDA, Deep learning, Matrix multiplication, nVidia, nVidia GeForce GTX Titan X

May 26, 2018 by hgpu

One machine, one minute, three billion tetrahedra

Celestin Marot, Jeanne Pellerin, Jean-Francois Remacle

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Tags: Computational Geometry, Computer science, Graphics, Intel Xeon Phi, Package

May 26, 2018 by hgpu

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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high performance computing on graphics processing units: hgpu.org

Applications

Optimizing Sparse Matrix-Vector Multiplication on Emerging Many-Core Architectures

A High-efficiency FPGA-based Accelerator for Convolutional Neural Networks using Winograd Algorithm

Fast Locality Sensitive Hashing for Beam Search on GPU

clMF: A fine-grained and portable alternating least squares algorithm for parallel matrix factorization

Design of FPGA-Based Accelerator for Convolutional Neural Network under Heterogeneous Computing Framework with OpenCL

NengoDL: Combining deep learning and neuromorphic modelling methods

Marian: Cost-effective High-Quality Neural Machine Translation in C++

OpenCL 2.2 API Specification

Transformations of High-Level Synthesis Codes for High-Performance Computing

Learning to Optimize Tensor Programs

One machine, one minute, three billion tetrahedra

Recent source codes

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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