high performance computing on graphics processing units: hgpu.org

Yangzihao Wang, Yuechao Pan, Andrew Davidson, Yuduo Wu, Carl Yang, Leyuan Wang, Muhammad Osama, Chenshan Yuan, Weitang Liu, Andy T. Riffel, John D. Owens

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Tags: Computer science, CUDA, Graph theory, nVidia, Package, Tesla K40

January 8, 2017 by hgpu

Deep Neural Networks to Enable Real-time Multimessenger Astrophysics

Daniel George, E. A. Huerta

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Tags: Algorithms, Artificial intelligence, Astrophysics, CUDA, Deep learning, Filtering, Gravitation, Gravitational waves, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080, Physics, Signal processing, Tesla K40, Tesla P100

January 4, 2017 by hgpu

Massively Parallel Computation of Accurate Densities for N-body Dark Matter Simulations using the Phase-Space-Element Method

Ralf Kaehler

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Tags: Astrophysics, Computational Physics, CUDA, Instrumentation and Methods for Astrophysics, N-body simulation, nVidia, Package, Physics, Tesla K80, Tessellation

January 4, 2017 by hgpu

Parallel Digital Predistortion Design on Mobile GPU and Embedded Multicore CPU for Mobile Transmitters

Kaipeng Li, Amanullah Ghazi, Chance Tarver, Jani Boutellier, Mahmoud Abdelaziz, Lauri Anttila, Markku Juntti, Mikko Valkama, Joseph R. Cavallaro

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Tags: ARM, CUDA, nVidia, nVidia Jetson TK1, nVidia Tegra TX1, Signal processing, SoC

December 31, 2016 by hgpu

Language Modeling with Gated Convolutional Networks

Yann N. Dauphin, Angela Fan, Michael Auli, David Grangier

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Tags: Computer science, CUDA, Deep learning, NLP, nVidia, Performance, Tesla M40, Torch

December 26, 2016 by hgpu

Batched Shift Reduce Parsing with Lists of Vectors on CUDA

Andrew Drozdov

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Tags: Computer science, CUDA, Deep learning, NLP, nVidia, nVidia GeForce GTX Titan Black, Package, Torch

December 26, 2016 by hgpu

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

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

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Agentic Code Optimization via Compiler-LLM Cooperation

See all packages

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high performance computing on graphics processing units: hgpu.org

DeepDSL: A Compilation-based Domain-Specific Language for Deep Learning

A Framework for Dense Triangular Matrix Kernels on Various Manycore Architectures

Synchronization and Coordination in Heterogeneous Processors

Akid: A Library for Neural Network Research and Production from a Dataism Approach

Communication and Coordination Paradigms for Highly-Parallel Accelerators

Gunrock: GPU Graph Analytics

Deep Neural Networks to Enable Real-time Multimessenger Astrophysics

Massively Parallel Computation of Accurate Densities for N-body Dark Matter Simulations using the Phase-Space-Element Method

Parallel Digital Predistortion Design on Mobile GPU and Embedded Multicore CPU for Mobile Transmitters

Language Modeling with Gated Convolutional Networks

Batched Shift Reduce Parsing with Lists of Vectors on CUDA

Recent source codes

CUDAnalyst (CUDA + Analyst)

CodegenBench

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

CUDA Kernel Fusion Benchmarks

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

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

Device Virtual Machine (DVM)

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Agentic Code Optimization via Compiler-LLM Cooperation

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