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Debunking the CUDA Myth Towards GPU-based AI Systems

Yunjae Lee, Juntaek Lim, Jehyeon Bang, Eunyeong Cho, Huijong Jeong, Taesu Kim, Hyungjun Kim, Joonhyung Lee, Jinseop Im, Ranggi Hwang, Se Jung Kwon, Dongsoo Lee, Minsoo Rhu

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Tags: AI, Benchmarking, Computer science, CUDA, Intel, Intel Gaudi-2, nVidia, nVidia A100, Performance

January 6, 2025 by hgpu

Performant Automatic BLAS Offloading on Unified Memory Architecture with OpenMP First-Touch Style Data Movement

Junjie Li

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Tags: Computer science, CUDA, HPC, Linear Algebra, nVidia, nVidia GH200, OpenMPI, Physics, Quantum Physics

January 6, 2025 by hgpu

Scalable Access-Pattern Aware I/O Acceleration and Multi-Tiered Data Management for HPC and AI Workloads

Avinash Maurya

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Tags: AI, Artificial intelligence, Computer science, CUDA, Deep learning, HPC, nVidia, nVidia DGX-A100, Package, Thesis

December 29, 2024 by hgpu

Asynchronous-Many-Task Systems: Challenges and Opportunities – Scaling an AMR Astrophysics Code on Exascale machines using Kokkos and HPX

Gregor Daiß, Patrick Diehl, Jiakun Yan, John K. Holmen, Rahulkumar Gayatri, Christoph Junghans, Alexander Straub, Jeff R. Hammond, Dominic Marcello, Miwako Tsuji, Dirk Pflüger, Hartmut Kaiser

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Tags: AMD Radeon Instinct MI100, AMD Radeon Instinct MI250X, Astrophysics, ATI, Computer science, CUDA, Heterogeneous systems, HIP, HPC, nVidia, nVidia A100, Package, performance portability, Physics

December 29, 2024 by hgpu

A survey on FPGA-based accelerator for ML models

Feng Yan, Andreas Koch, Oliver Sinnen

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Tags: Computer science, FPGA, Hardware Architecture, Machine learning, survey

December 29, 2024 by hgpu

Development of a new framework for high performance volunteer computing

Ehab Saleh, B. S. Chandrasekar

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Tags: Computer science, Distributed computing, Heterogeneous computing, HPC

December 29, 2024 by hgpu

Utilizing Tensor Cores in Futhark

Kristoffer August Kortbæk, Rune Ejnar Bang Lejbølle

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, High-level Languages, Matrix multiplication, nVidia, nVidia A100, Package, PTX

December 24, 2024 by hgpu

CPPJoules: An Energy Measurement Tool for C++

Shivadharshan S, Akilesh P, Rajrupa Chattaraj, Sridhar Chimalakonda

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, Package, Software Engineering

December 24, 2024 by hgpu

Accelerating Sparse Graph Neural Networks with Tensor Core Optimization

Ka Wai Wu

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Tags: Computer science, CUDA, Graph, Heterogeneous systems, Neural networks, nVidia, PyTorch, Tesla V100

December 24, 2024 by hgpu

HPC-Coder-V2: Studying Code LLMs Across Low-Resource Parallel Languages

Aman Chaturvedi, Daniel Nichols, Siddharth Singh, Abhinav Bhatele

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Tags: Code generation, Computer science, CUDA, HIP, HPC, LLM, MPI, nVidia, nVidia A100, OpenMP, Package

December 24, 2024 by hgpu

Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

Ezhilmathi Krishnasamy, Pascal Bouvry

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Tags: BLAS, Computer science, CUDA, Differential equations, HPC, Linear Algebra, Matrix multiplication, nVidia, nVidia A100, OpenACC, Package, Partial differential equations, PDEs, Performance

December 24, 2024 by hgpu

Analyzing the Performance Portability of SYCL across CPUs, GPUs, and Hybrid Systems with Protein Database Search

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

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Tags: AMD Radeon RX 6700 XT, AMD Radeon RX Vega 6, ATI, Bioinformatics, Biology, Computer science, CUDA, Databases, Heterogeneous systems, HPC, Intel, Intel Arc A770, Intel UHD 630, Intel UHD 770, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, nVidia GeForce RTX 2070, nVidia GeForce RTX 3070, nVidia GeForce RTX 3090, oneAPI, Package, performance portability, SYCL, Tesla V100

December 15, 2024 by hgpu

True 4-Bit Quantized CNN Training on CPU

True 4-Bit Quantized Convolutional Neural Network Training on CPU: Achieving Full-Precision Parity

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

Hunting CUDA Bugs at Scale with cuFuzz

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

CONCUR: a benchmark designed to evaluate multithreaded Java code generated by LLMs

CONCUR: Benchmarking LLMs for Concurrent Code Generation

HIPRT: Ray Tracing using HIP

Ray Tracing using HIP

MXFP4 Training Support Codebase

Practical FP4 Training for Large-Scale MoE Models on Hopper GPUs

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