high performance computing on graphics processing units: hgpu.org

hgpu.org » nVidia GeForce GXT 980

Performance Analysis and Automatic Tuning of Hash Aggregation on GPUs

Viktor Rosenfeld, Sebastian Bress, Steffen Zeuch, Tilmann Rabl, Volker Markl

View

Tags: Algorithms, AMD Radeon R9 Fury, ATI, Computer science, Hashing, nVidia, nVidia GeForce GXT 1080, nVidia GeForce GXT 980, OpenCL, Performance, Tesla K40, Tesla V100

June 16, 2019 by hgpu

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

ConCuR: Conciseness Makes State-of-the-Art Kernel Generation

VibeCodeHPC - Multi Agentic Vibe Coding for HPC

VibeCodeHPC: An Agent-Based Iterative Prompting Auto-Tuner for HPC Code Generation Using LLMs

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

exa-AMD: Exascale Accelerated Materials Discovery

exa-AMD: An Exascale-Ready Framework for Accelerating the Discovery and Design of Functional Materials

TRUST: a thermalhydraulic software package for CFD simulations

TRUST: the HPC open-source CFD platform – from CPU to GPU

Modular: The Modular Platform (includes MAX & Mojo)

Mojo: MLIR-Based Performance-Portable HPC Science Kernels on GPUs for the Python Ecosystem

Allo: Accelerator Design Language

Dato: A Task-Based Programming Model for Dataflow Accelerators

Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization

Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization

See all packages

* * *

* * *

HGPU group © 2010-2025 hgpu.org

All rights belong to the respective authors

Login | Sitemap | Feedback | Policy

Contact us: