high performance computing on graphics processing units: hgpu.org

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Optimal Kernel Orchestration for Tensor Programs with Korch

Muyan Hu, Ashwin Venkatram, Shreyashri Biswas, Balamurugan Marimuthu, Bohan Hou, Gabriele Oliaro, Haojie Wang, Liyan Zheng, Xupeng Miao, Jidong Zhai

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Tags: Compilers, Computer science, CUDA, Neural networks, nVidia, nVidia A100, nVidia V100, Package, PyTorch

June 23, 2024 by hgpu

COOK Access Control on an embedded Volta GPU

Benjamin Lesage, Frédéric Boniol, Claire Pagetti

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Tags: Computer science, CUDA, Machine learning, Neural networks, nVidia, nVidia Jetson AGX Xavier

June 23, 2024 by hgpu

How much can we gain from Tensor Kernel Fusion on GPUs?

Wei Sun, Ang Li, Sander Stuijk, Henk Corporaal

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Tags: Computer science, CUDA, Deep learning, Matrix multiplication, Neural networks, nVidia, nVidia A100, nVidia H100

June 16, 2024 by hgpu

Evaluation of computational and energy performance in matrix multiplication algorithms on CPU and GPU using MKL, cuBLAS and SYCL

L.A. Torres, Carlos J. Barrios H, Yves Denneulin

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, Neural networks, nVidia, nVidia A100, Package, Performance, SYCL

June 2, 2024 by hgpu

Kernel-Centric Optimizations for Deep Neural Networks on GPGPU

Zhaodong Chen

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Tags: Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia A100, nVidia V100, Thesis

May 26, 2024 by hgpu

From GPUs to AI and quantum: three waves of acceleration in bioinformatics

Bertil Schmidt, Andreas Hildebrandt

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Tags: Artificial intelligence, Bioinformatics, Biology, Chemistry, Neural networks, Quantum computing

May 20, 2024 by hgpu

High Performance Privacy Preserving AI

Jayavanth Shenoy, Patrick Grinaway, Shriphani Palakodety

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Tags: AI, Artificial intelligence, Book, Computer science, Neural networks, Security

April 14, 2024 by hgpu

Using AI libraries for Incompressible Computational Fluid Dynamics

Boyang Chen, Claire E. Heaney, Christopher C. Pain

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Tags: AI, Artificial intelligence, cfd, CUDA, Fluid dynamics, Neural networks, nVidia, Partial differential equations, PDEs, Tesla T4

March 3, 2024 by hgpu

Spyx: A Library for Just-In-Time Compiled Optimization of Spiking Neural Networks

Kade M. Heckel, Thomas Nowotny

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Tags: Artificial intelligence, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia RTX A6000, Package, Python

March 3, 2024 by hgpu

TransAxx: Efficient Transformers with Approximate Computing

Dimitrios Danopoulos, Georgios Zervakis, Dimitrios Soudris, Jörg Henkel

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Tags: Computer science, CUDA, Machine learning, Neural networks, nVidia, nVidia V100, Package, PyTorch

February 18, 2024 by hgpu

Training DNN Models over Heterogeneous Clusters with Optimal Performance

Chengyi Nie, Jessica Maghakian, Zhenhua Liu

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, load balancing, Neural networks, nVidia, nVidia RTX 6000 Ada

February 12, 2024 by hgpu

A Heterogeneous Inference Framework for a Deep Neural Network

Rafael Gadea-Gironés, José Luís Rocabado-Rocha, Jorge Fe, Jose M. Monzo

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Tags: Artificial intelligence, Computer science, Deep learning, FPGA, Heterogeneous systems, HLS, Machine learning, Neural networks, OpenCL, PyTorch

January 28, 2024 by hgpu

GPUODEBenchmarks: Comparsion of Julia's GPU Kernel based ODE solvers with other open-source GPU ODE solvers

Automating Heterogeneous Parallelism in Numerical Differential Equations

Dat3M: Memory Model Aware Verification

Towards Unified Analysis of GPU Consistency

NVIDIA Federated Learning Application Runtime Environment

Supercharging Federated Learning with Flower and NVIDIA FLARE

Chat AI: A Seamless Slurm-Native Solution for HPC-Based Services

PSCToolkit: solving sparse linear systems with a large number of GPUs

CATBench: Benchmarking Framework

CATBench: A Compiler Autotuning Benchmarking Suite for Black-box Optimization

General-purpose Polyhedral Compilers

A Survey of General-purpose Polyhedral Compilers

Optimal Kernel Orchestration for Tensor Programs with Korch

Astaroth: A Scalable Multi-GPU Library for Stencil Computations

Stencil Computations on AMD and Nvidia Graphics Processors: Performance and Tuning Strategies

Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs. Empirical tricks for LLM Jailbreaking

Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs

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high performance computing on graphics processing units: hgpu.org

Optimal Kernel Orchestration for Tensor Programs with Korch

COOK Access Control on an embedded Volta GPU

How much can we gain from Tensor Kernel Fusion on GPUs?

Evaluation of computational and energy performance in matrix multiplication algorithms on CPU and GPU using MKL, cuBLAS and SYCL

Kernel-Centric Optimizations for Deep Neural Networks on GPGPU

From GPUs to AI and quantum: three waves of acceleration in bioinformatics

High Performance Privacy Preserving AI

Using AI libraries for Incompressible Computational Fluid Dynamics

Spyx: A Library for Just-In-Time Compiled Optimization of Spiking Neural Networks

TransAxx: Efficient Transformers with Approximate Computing

Training DNN Models over Heterogeneous Clusters with Optimal Performance

A Heterogeneous Inference Framework for a Deep Neural Network

Recent source codes

GPUODEBenchmarks: Comparsion of Julia's GPU Kernel based ODE solvers with other open-source GPU ODE solvers

Dat3M: Memory Model Aware Verification

NVIDIA Federated Learning Application Runtime Environment

Chat AI: A Seamless Slurm-Native Solution for HPC-Based Services

PSCToolkit: solving sparse linear systems with a large number of GPUs

CATBench: Benchmarking Framework

General-purpose Polyhedral Compilers

Optimal Kernel Orchestration for Tensor Programs with Korch

Astaroth: A Scalable Multi-GPU Library for Stencil Computations

Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs. Empirical tricks for LLM Jailbreaking

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