high performance computing on graphics processing units: hgpu.org

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Building a Performance Model for Deep Learning Recommendation Model Training on GPUs

Zhongyi Lin, Louis Feng, Ehsan K. Ardestani, Jaewon Lee, John Lundell, Changkyu Kim, Arun Kejariwal, John D. Owens

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Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX Titan XP, Package, Performance, Tesla P100, Tesla V100

January 23, 2022 by hgpu

Fancier: A Unified Framework for Java, C, and OpenCL Integration

Sergio Afonso, Francisco Almeida

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Tags: Computer science, Image processing, Java, OpenACC, OpenCL, Package, Performance

January 16, 2022 by hgpu

A Compiler Framework for Optimizing Dynamic Parallelism on GPUs

Mhd Ghaith Olabi, Juan Gómez Luna, Onur Mutlu, Wen-mei Hwu, Izzat El Hajj

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Tags: Compilers, Computer science, CUDA, nVidia, Package, Performance, Tesla V100

January 16, 2022 by hgpu

Reveal training performance mystery between TensorFlow and PyTorch in the single GPU environment

Hulin Dai, Xuan Peng, Xuanhua Shi, Ligang He, Qian Xiong, Hai Jin

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Tags: Benchmarking, Computer science, CUDA, Deep learning, Neural networks, nVidia, Performance, PyTorch, TensorFlow, Tesla P100

January 9, 2022 by hgpu

A Variant of Concurrent Constraint Programming on GPU

Pierre Talbot, Frederic Pinel, Pascal Bouvry

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Tags: Computer science, CUDA, nVidia, nVidia Quadro RTX 5000, Package, Performance

January 2, 2022 by hgpu

PROGRAML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations

Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Michael O'Boyle, Hugh Leather

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Tags: Compilers, Computer science, Flow analysis, Machine learning, OpenCL, Package, Performance

January 2, 2022 by hgpu

Optimization of Compiler-generated OpenCL CNN Kernels and Runtime for FPGAs

Seung-Hun Chung

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Tags: Computer science, FPGA, Neural networks, nVidia, nVidia GeForce GTX 1060, OpenCL, Performance, Thesis

December 19, 2021 by hgpu

Analysis and Comparison of Performance and Power Consumption of Neural Networks on CPU, GPU, TPU and FPGA

Christopher Noel Hesse

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Tags: Benchmarking, Computer science, Deep learning, FPGA, Neural networks, nVidia, nVidia DGX-1, nVidia GeForce RTX 2070, nVidia Jetson Nano, Performance, Tesla V100

December 5, 2021 by hgpu

Bayesian Optimization for auto-tuning GPU kernels

Floris-Jan Willemsen, Rob van Nieuwpoort, Ben van Werkhoven

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Tags: Bayesian, Computer science, CUDA, Machine learning, nVidia, nVidia A100, nVidia GeForce GTX Titan X, nVidia GeForce RTX 2070, OpenCL, Package, Performance

December 5, 2021 by hgpu

Generating GPU Compiler Heuristics using Reinforcement Learning

Ian Colbert, Jake Daly, Norm Rubin

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Tags: AMD Radeon 6800 XT, AMD Radeon RX 5700 XT, ATI, Compilers, Computer science, Machine learning, Performance

November 28, 2021 by hgpu

Predictive Data Race Detection for GPUs

Sagnik Dey, Mayant Mukul, Parth Sharma, Swarnendu Biswas

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia Quadro RTX 5000, Performance

November 28, 2021 by hgpu

BAT: A Benchmark suite for AutoTuners

Ingunn Sund, Knut A. Kirkhorn, Jacob O. Tørring, Anne C. Elster

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Tags: Auto-Tuning, Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 980, Package, Performance, Tesla T4, Tesla V100

November 21, 2021 by hgpu

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

MSCCL++: Rethinking GPU Communication Abstractions for Cutting-edge AI Applications

Benchmark compute shader of Unity against InteropUnityCUDA

InteropUnityCUDA: A Tool for Interoperability Between Unity and CUDA

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

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

GigaAPI for GPU Parallelization

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

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high performance computing on graphics processing units: hgpu.org

Building a Performance Model for Deep Learning Recommendation Model Training on GPUs

Fancier: A Unified Framework for Java, C, and OpenCL Integration

A Compiler Framework for Optimizing Dynamic Parallelism on GPUs

Reveal training performance mystery between TensorFlow and PyTorch in the single GPU environment

A Variant of Concurrent Constraint Programming on GPU

PROGRAML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations

Optimization of Compiler-generated OpenCL CNN Kernels and Runtime for FPGAs

Analysis and Comparison of Performance and Power Consumption of Neural Networks on CPU, GPU, TPU and FPGA

Bayesian Optimization for auto-tuning GPU kernels

Generating GPU Compiler Heuristics using Reinforcement Learning

Predictive Data Race Detection for GPUs

BAT: A Benchmark suite for AutoTuners

Recent source codes

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

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

DuoReduce: MLIR's benchmark

Shamrock: Multi-GPU hydrodynamics for astrophysics

LLMPerf: GPU Performance Modeling meets Large Language Models

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