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Predicting the Execution Time of a kernel on a specific GPU using PTX code

Monika Dagar, Jorge Roldan

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX 1650, nVidia GeForce GTX Titan XP, Performance, PTX, Tesla K20, Tesla K80, Tesla M60, Tesla P100, Tesla T4, Tesla V100

October 22, 2023 by hgpu

Fortran performance optimisation and auto-parallelisation by leveraging MLIR-based domain specific abstractions in Flang

Nick Brown, Maurice Jamieson, Anton Lydike, Emilien Bauer, Tobias Grosser

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Tags: Computer science, Fortran, HPC, LLVM, MPI, nVidia, Package, Performance, Tesla V100

October 8, 2023 by hgpu

Novel Parallelization Strategies for High-Performance DNN Training on HPC Systems

Arpan Jain

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Tags: Artificial intelligence, Computer science, CUDA, Data parallelism, Deep learning, HPC, Machine learning, MPI, Neural networks, nVidia, Speech recognition, Tesla K80, Tesla V100, Thesis

October 1, 2023 by hgpu

Comparing Performance and Portability between CUDA and SYCL for Protein Database Search on NVIDIA, AMD, and Intel GPUs

Manuel Costanzo, Enzo Rucci, Carlos García Sánchez, Marcelo Naiouf, Manuel Prieto-Matías

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Tags: AMD Radeon RX 6700 XT, ATI, Bioinformatics, Biology, Computer science, CUDA, Intel, Intel UHD 630, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, nVidia GeForce RTX 2070, nVidia GeForce RTX 3070, nVidia GeForce RTX 3090, oneAPI, Programming Languages, Smith-Waterman algorithm, SYCL, Tesla V100

September 24, 2023 by hgpu

Mystique: Enabling Accurate and Scalable Generation of Production AI Benchmarks

Mingyu Liang, Wenyin Fu, Louis Feng, Zhongyi Lin, Pavani Panakanti, Shengbao Zheng, Srinivas Sridharan, Christina Delimitrou

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Tags: AI, Benchmarking, Code generation, Computer science, CUDA, nVidia, Package, Performance, PyTorch, Tesla A100, Tesla V100

July 16, 2023 by hgpu

Towards Intelligent Runtime Framework for Distributed Heterogeneous Systems

Polykarpos Thomadakis

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Tags: Computer science, CUDA, Heterogeneous systems, Machine learning, Nuclear physics, nVidia, OpenCL, Physics, Task scheduling, Tesla V100, Thesis

July 16, 2023 by hgpu

Managing, Profiling, and Optimizing Heterogeneous GPU Workloads

James Gleeson

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Tags: Computer science, CUDA, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce GTX 480, nVidia GeForce RTX 2080 Ti, nVidia Grid K1, Optimization, Package, Performance, Tesla V100, Thesis, Virtualization

July 2, 2023 by hgpu

Reducing branch divergence to speed up parallel execution of unit testing on GPUs

Taghreed Bagies, Wei Le, Jeremy Sheafer, Ali Jannesari

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

June 18, 2023 by hgpu

Program Analysis and Machine Learning based Approach to Predict Power Consumption of CUDA Kernel

Gargi Alavani, Jineet Desai, Snehanshu Saha, Santonu Sarkar

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Tags: Benchmarking, Computer science, CUDA, Energy-efficient computing, Machine learning, nVidia, Package, Tesla K20, Tesla M60, Tesla V100

June 11, 2023 by hgpu

Understanding Performance Portability of Bioinformatics Applications in SYCL on an NVIDIA GPU

Zheming Jin, Jeffrey S. Vetter

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Tags: Benchmarking, Bioinformatics, Computer science, CUDA, Heterogeneous systems, nVidia, Package, performance portability, SYCL, Tesla V100

April 16, 2023 by hgpu

Energy-Efficient GPU Clusters Scheduling for Deep Learning

Diandian Gu, Xintong Xie, Gang Huang, Xin Jin, Xuanzhe Liu

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Tags: Computer science, Deep learning, Energy-efficient computing, GPU cluster, Neural networks, nVidia, Tesla V100

April 16, 2023 by hgpu

ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales

Xingfu Wu, Prasanna Balaprakash, Michael Kruse, Jaehoon Koo, Brice Videau, Paul Hovland, Valerie Taylor, Brad Geltz, Siddhartha Jana, Mary Hall

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Tags: Bayesian, Computer science, Energy-efficient computing, MPI, nVidia, OpenMP, Package, Tesla V100

April 2, 2023 by hgpu

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

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

Shamrock: Multi-GPU hydrodynamics for astrophysics

The Shamrock code: I- Smoothed Particle Hydrodynamics on GPUs

LLMPerf: GPU Performance Modeling meets Large Language Models

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

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high performance computing on graphics processing units: hgpu.org

Predicting the Execution Time of a kernel on a specific GPU using PTX code

Fortran performance optimisation and auto-parallelisation by leveraging MLIR-based domain specific abstractions in Flang

Novel Parallelization Strategies for High-Performance DNN Training on HPC Systems

Comparing Performance and Portability between CUDA and SYCL for Protein Database Search on NVIDIA, AMD, and Intel GPUs

Mystique: Enabling Accurate and Scalable Generation of Production AI Benchmarks

Towards Intelligent Runtime Framework for Distributed Heterogeneous Systems

Managing, Profiling, and Optimizing Heterogeneous GPU Workloads

Reducing branch divergence to speed up parallel execution of unit testing on GPUs

Program Analysis and Machine Learning based Approach to Predict Power Consumption of CUDA Kernel

Understanding Performance Portability of Bioinformatics Applications in SYCL on an NVIDIA GPU

Energy-Efficient GPU Clusters Scheduling for Deep Learning

ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales

Recent source codes

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

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

Celerity Runtime: High-level C++ for Accelerator Clusters

wgpy: WebGL accelerated numpy-compatible array library for web browser

Microbenchmarking OpenMP target offload with Catch2

SUperman: Highly Efficient Permanent Computation Library

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

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