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Column-Oriented Datalog on the GPU

Yihao Sun, Sidharth Kumar, Thomas Gilray, Kristopher Micinski

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Tags: Computer science, CUDA, Databases, nVidia, nVidia H100, Package

January 27, 2025 by hgpu

Code Generation for Cryptographic Kernels using Multi-word Modular Arithmetic on GPU

Naifeng Zhang, Franz Franchetti

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Tags: Code generation, Computer science, CUDA, Linear Algebra, Modular arithmetic, nVidia, nVidia GeForce RTX 4090, nVidia H100, nVidia V100, Package, Security

January 20, 2025 by hgpu

Boosting Performance of Iterative Applications on GPUs: Kernel Batching with CUDA Graphs

Jonah Ekelund, Stefano Markidis, Ivy Peng

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Tags: Computer science, CUDA, FDTD, HIP, nVidia, nVidia A100, nVidia H100, Package, Performance

January 20, 2025 by hgpu

CGP-Tuning: Structure-Aware Soft Prompt Tuning for Code Vulnerability Detection

Ruijun Feng, Hammond Pearce, Pietro Liguori, Yulei Sui

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Tags: Computer science, CUDA, LLM, nVidia, nVidia H100, Python, PyTorch, Security

January 13, 2025 by hgpu

Data Parallel Visualization and Rendering on the RAMSES Supercomputer with ANARI

Stefan Zellmann

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Tags: 3D Graphics and Realism, HPC, MPI, nVidia, nVidia H100, Package, Rendering, Visualization

January 13, 2025 by hgpu

Unified schemes for directive-based GPU offloading

Yohei Miki, Toshihiro Hanawa

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Tags: AMD Radeon Instinct MI210, ATI, Computer science, Diffusion equation, Intel, Intel Ponte Vecchio Max 1100, N-body simulation, nVidia, nVidia GH200, nVidia H100, OpenACC, OpenMP, Package

December 8, 2024 by hgpu

Scaling SU(2) to 1000 GPUs using HiRep

Sofie Martins, Erik Kjellgren, Emiliano Molinaro, Claudio Pica, Antonio Rago

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Tags: AMD Radeon Instinct MI250X, ATI, CUDA, HEP, High Energy Physics - Lattice, HIP, Monte Carlo simulation, nVidia, nVidia H100, Package, Physics

December 1, 2024 by hgpu

A Distributed-memory Tridiagonal Solver Based on a Specialised Data Structure Optimised for CPU and GPU Architectures

Semih Akkurt, Sébastien Lemaire, Paul Bartholomew, Sylvain Laizet

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Tags: Computational Physics, Computer science, CUDA, nVidia, nVidia A100, nVidia GH200, nVidia H100, nVidia V100, OpenMP, PDEs

November 24, 2024 by hgpu

gpuPairHMM: High-speed Pair-HMM Forward Algorithm for DNA Variant Calling on GPUs

Bertil Schmidt, Felix Kallenborn, Alexander Wichmann, Alejandro Chacon, Christian Hundt

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Tags: Bioinformatics, Biology, Computer science, CUDA, FPGA, Genomics, nVidia, nVidia A100, nVidia H100, nVidia L4, nVidia L40s, nVidia V100, Package

November 24, 2024 by hgpu

Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case study

Giulio Malenza, Valentina Cesare, Marco Edoardo Santimaria, Robert Birke, Alberto Vecchiato, Ugo Becciani, Marco Aldinucci

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Tags: AMD Radeon Instinct MI250X, Astrophysics, ATI, Computer science, CUDA, HIP, HPC, nVidia, nVidia A100, nVidia H100, nVidia V100, OpenMP, Package, Performance, performance portability, SYCL, Tesla T4

November 24, 2024 by hgpu

Context Parallelism for Scalable Million-Token Inference

Amy (Jie) Yang, Jingyi Yang, Aya Ibrahim, Xinfeng Xie, Bangsheng Tang, Grigory Sizov, Jeremy Reizenstein, Jongsoo Park, Jianyu Huang

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Tags: Artificial intelligence, Benchmarking, Computer science, LLM, nVidia, nVidia H100, Performance

November 17, 2024 by hgpu

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

Krishna Teja Chitty-Venkata, Siddhisanket Raskar, Bharat Kale, Farah Ferdaus, Aditya Tanikanti, Ken Raffenetti, Valerie Taylor, Murali Emani, Venkatram Vishwanath

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Tags: AI, AMD Radeon Instinct MI250, AMD Radeon Instinct MI300X, Artificial intelligence, ATI, Benchmarking, Computer science, CUDA, LLM, Machine learning, nVidia, nVidia A100, nVidia GH200, nVidia H100, OpenCL, Performance

November 10, 2024 by hgpu

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

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

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