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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

Machine Learning-Driven Adaptive OpenMP For Portable Performance on Heterogeneous Systems

Giorgis Georgakoudis, Konstantinos Parasyris, Chunhua Liao, David Beckingsale, Todd Gamblin, Bronis de Supinski

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Tags: AMD Radeon Instinct Mi50, ATI, Benchmarking, Code generation, Compilers, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, OpenMP, performance portability, Tesla P100, Tesla V100

March 19, 2023 by hgpu

Runtime Support for Performance Portability on Heterogeneous Distributed Platforms

Polykarpos Thomadakis, Nikos Chrisochoides

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Tags: Computer science, CUDA, Heterogeneous systems, MPI, nVidia, performance portability, Tesla V100

March 12, 2023 by hgpu

EvoTorch: Scalable Evolutionary Computation in Python

Nihat Engin Toklu, Timothy Atkinson, Vojtěch Micka, Paweł Liskowski, Rupesh Kumar Srivastava

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Tags: Artificial intelligence, Computer science, CUDA, Evolutionary Computations, Neural and Evolutionary Computing, nVidia, Package, Python, PyTorch, Tesla V100

March 5, 2023 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

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

Machine Learning-Driven Adaptive OpenMP For Portable Performance on Heterogeneous Systems

Runtime Support for Performance Portability on Heterogeneous Distributed Platforms

EvoTorch: Scalable Evolutionary Computation in Python

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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