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Towards a Benchmarking Suite for Kernel Tuners

Jacob O. Tørring, Ben van Werkhoven, Filip Petrovic, Floris-Jan Willemsen, Jirí Filipovic, Anne C. Elster

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Tags: Auto-Tuning, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce RTX 2080 Ti, nVidia GeForce RTX 3060, nVidia GeForce RTX 3090, nVidia Titan RTX, Package, performance portability

March 19, 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

Harmonic CUDA: Asynchronous Programming on GPUs

Jonathan Wapman, Sean Treichler, Serban D. Porumbescu, John D. Owens

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Tags: Computer science, CUDA, GEMM, Linear Algebra, Matrix multiplication, nVidia, nVidia A100

March 5, 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

RTIndeX: Exploiting Hardware-Accelerated GPU Raytracing for Database Indexing

Justus Henneberg, Felix Schuhknecht

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Tags: Computer science, CUDA, Databases, nVidia, nVidia RTX A6000, Package, Raytracing

March 5, 2023 by hgpu

Porting numerical integration codes from CUDA to oneAPI: a case study

Ioannis Sakiotis, Kamesh Arumugam, Marc Paterno, Desh Ranjan, Balsa Terzic, Mohammad Zubair

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Tags: Computer science, CUDA, nVidia, nVidia V100, Package, SYCL

February 26, 2023 by hgpu

Auto-SpMV: Automated Optimizing SpMV Kernels on GPU

Mina Ashoury, Mohammad Loni, Farshad Khunjush, Masoud Daneshtalab

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Tags: Computer science, CUDA, Energy efficiency, Linear Algebra, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 1650, Sparse matrix

February 26, 2023 by hgpu

Orca: FSS-based Secure Training with GPUs

Neha Jawalkar, Kanav Gupta, Arkaprava Basu, Nishanth Chandran, Divya Gupta, Rahul Sharma

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Tags: Computer science, CUDA, nVidia, nVidia RTX A6000, Security

February 26, 2023 by hgpu

GPU Offloading in ExaHyPE Through C++ Standard Algorithms

Uzmar Gomez, Gonzalo Brito Gadeschi, Tobias Weinzierl

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Tags: Algorithms, Computer science, CUDA, Differential equations, Mathematical Software, nVidia, nVidia GeForce RTX 2080 Ti, OpenMP, Partial differential equations, PDEs

February 26, 2023 by hgpu

TLP: A Deep Learning-based Cost Model for Tensor Program Tuning

Yi Zhai, Yu Zhang, Shuo Liu, Xiaomeng Chu, Jie Peng, Jianmin Ji, Yanyong Zhang

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Tags: Computer science, CUDA, Deep learning, nVidia, Optimization, Package, Tesla K80, Tesla T4

February 12, 2023 by hgpu

ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills

Jiayuan Gu, Fanbo Xiang, Xuanlin Li, Zhan Ling, Xiqiang Liu, Tongzhou Mu, Yihe Tang, Stone Tao, Xinyue Wei, Yunchao Yao, Xiaodi Yuan, Pengwei Xie, Zhiao Huang, Rui Chen, Hao Su

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia Titan RTX, Package, Rendering, Robotics

February 12, 2023 by hgpu

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

Kokkidio: Fast, expressive, portable code, based on Kokkos and Eigen

LLaMA-Inference-Bench

LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators

MATAR

On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures

Over-synchronization in GPU Programs

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

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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