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A domain-specific language for geospatial computations on the GPU

Louise Tidestav

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Tags: Computer science, CUDA, DSL, nVidia, nVidia Quadro P 2000, Thesis

October 13, 2024 by hgpu

Efficient Arbitrary Precision Acceleration for Large Language Models on GPU Tensor Cores

Shaobo Ma, Chao Fang, Haikuo Shao, Zhongfeng Wang

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Tags: Computer science, CUDA, Data recovery, Extended precision, LLM, Matrix multiplication, nVidia, nVidia GeForce RTX 3090, Security

October 6, 2024 by hgpu

Event-Based OpenMP Tasks for Time-Sensitive GPU-Accelerated Systems

Cyril Cetre, Chenle Yu, Sara Royuela, Rémi Barrere, Eduardo Quiñones, Damien Gratadour

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Tags: AMD Radeon Instinct MI100, ATI, Computer science, CUDA, HIP, HPC, nVidia, nVidia A100, Performance

October 6, 2024 by hgpu

Benchmarking Thread Block Cluster

Tim Lühnen, Tobias Marschner, Sohan Lal

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia H100, PTX

October 6, 2024 by hgpu

HPC acceleration of large (min, +) matrix products to compute domination-type parameters in graphs

E.M. Garzón, J.A. Martínez, J.J. Moreno, M.L. Puertas

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Tags: Algorithms, Computer science, CUDA, Graph theory, HPC, nVidia, OpenMP, Package, Tesla V100

September 29, 2024 by hgpu

Automatic Generation of OpenCL Code through Polyhedral Compilation with LLM

Marek Palkowski, Mateusz Gruzewski

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Tags: AMD Radeon RX 6800, ATI, Code generation, Computer science, CUDA, LLM, nVidia, nVidia A100, OpenCL, OpenMP

September 29, 2024 by hgpu

A Study of Performance Programming of CPU, GPU accelerated Computers and SIMD Architecture

Xinyao Yi

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Tags: Computer science, CUDA, Heterogeneous systems, HPC, nVidia, nVidia A100, OpenMP, Performance, Programming techniques

September 22, 2024 by hgpu

DNA sequence alignment: An assignment for OpenMP, MPI, and CUDA/OpenCL

Arturo Gonzalez-Escribano, Diego García-Álvarez, Jesús Cámara

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Tags: Bioinformatics, Biology, CUDA, MPI, nVidia, nVidia A100, nVidia GeForce GTX Titan X, nVidia GeForce RTX 4050, OpenCL, Optimization, Sequence alignment

September 15, 2024 by hgpu

Enhancing Code Portability, Problem Scale, and Storage Efficiency in Exascale Applicationsin Exascale Applications

Nigel Tan

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Tags: AMD Radeon Vega VII, ATI, Computer science, CUDA, Heterogeneous systems, HPC, nVidia, nVidia A100, nVidia DGX-A100, nVidia Quadro RTX 5000, OpenMP, OpenMPI, Particle-in-cell methods, performance portability, Tesla V100, Thesis

September 15, 2024 by hgpu

Refining HPCToolkit for application performance analysis at exascale

Laksono Adhianto, Jonathon Anderson, Robert Matthew Barnett, Dragana Grbic, Vladimir Indic, Mark Krentel, Yumeng Liu, Srdan Milakovíc, Wileam Phan, John Mellor-Crumme

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Tags: AMD Radeon Instinct MI300A, ATI, Computer science, CUDA, HPC, MPI, OpenCL, Package, Performance

September 15, 2024 by hgpu

Optimizing the Weather Research and Forecasting Model with OpenMP Offload and Codee

Chayanon (Namo)Wichitrnithed, Woo-Sun-Yang, Yun (Helen)He, Brad Richardson, Koichi Sakaguchi, Manuel Arenaz, William I. Gustafson Jr., Jacob Shpund, Ulises Costi Blanco, Alvaro Goldar Dieste

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Tags: Computer science, CUDA, Fortran, MPI, nVidia, nVidia A100, OpenMP, Optimization, Package, Weather prediction

September 15, 2024 by hgpu

A Parallel Compression Pipeline for Improving GPU Virtualization Data Transfers

Cristian Peñaranda, Carlos Reaño, Federico Silla

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Tags: Compression, Computer science, CUDA, Deep learning, nVidia, nVidia A100, Package, Virtualization

September 1, 2024 by hgpu

AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning

AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning

Probe-and-Refine Tuning of Repository Guidance for AI Coding Agents

Probe-and-Refine Tuning of Repository Guidance for Coding Agents

CUDAnalyst (CUDA + Analyst)

Towards Feedback-to-Plan Decisions for Self-Evolving LLM Agents in CUDA Kernel Generation

CodegenBench

CodegenBench: Can LLMs Write Efficient Code Across Architectures?

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

Analyzing the Impact of Kernel Fusion on GPU Tensor Operation Performance: A Systematic Performance Study

IntelliKit: Agent-first tooling for AMD hardware

Kerncap: Automated Kernel Extraction and Isolation for AMD GPUs

DITRON: Distributed Compiler based on Triton for Parallel Systems

DITRON: Distributed Multi-level Tiling Compiler for Parallel Tensor Programs

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

Agentic Code Optimization via Compiler-LLM Cooperation

Agentic Code Optimization via Compiler-LLM Cooperation

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