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FTTN: Feature-Targeted Testing for Numerical Properties of NVIDIA & AMD Matrix Accelerators

Xinyi Li, Ang Li, Bo Fang, Katarzyna Swirydowicz, Ignacio Laguna, Ganesh Gopalakrishnan

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Tags: AMD Radeon Instinct MI100, AMD Radeon Instinct MI250X, ATI, Computer science, Hardware Architecture, HPC, Matrix multiplication, nVidia, nVidia A100, nVidia H100, nVidia V100, PTX

March 10, 2024 by hgpu

SYCL-Bench 2020: Benchmarking SYCL 2020 on AMD, Intel, and NVIDIA GPUs

Luigi Crisci, Lorenzo Carpentieri, Peter Thoman, Aksel Alpay, Vincent Heuveline, Biagio Cosenza

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Tags: AMD Radeon Instinct MI100, ATI, Benchmarking, Computer science, Heterogeneous systems, HPC, Intel, Intel Ponte Vecchio Max 1100, nVidia, oneAPI, Portability, SYCL, Tesla V100

March 10, 2024 by hgpu

Parallel Implementation of Lightweight Secure Hash Algorithm on CPU and GPU Environments

Hojin Choi, SeongJun Choi, SeogChung Seo

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Tags: Computer science, CUDA, Hashing, nVidia, nVidia GeForce RTX 2080 Ti, nVidia GeForce RTX 3090, Package, PTX, Security

March 10, 2024 by hgpu

Sustainable Supercomputing for AI: GPU Power Capping at HPC Scale

Dan Zhao, Siddharth Samsi, Joseph McDonald, Baolin Li, David Bestor, Michael Jones, Devesh Tiwari, Vijay Gadepally

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Tags: AI, Computer science, Energy-efficient computing, HPC, nVidia, nVidia A100

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

Parallel programming in mobile devices with FancyJCL

Sergio Afonso, Óscar Gómez-Cárdenes, Paula Expósito, Vicente Blanco, Francisco Almeida

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Tags: Computer science, DSP, Heterogeneous systems, Image processing, Java, OpenCL, Package

March 3, 2024 by hgpu

Low-Overhead Trace Collection and Profiling on GPU Compute Kernels

Sébastien Darche, Michel R. Dagenais

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Tags: AMD Radeon Instinct MI100, ATI, Benchmarking, Computer science, CUDA, HIP, nVidia, Package, Performance, Profiling

March 3, 2024 by hgpu

APPy: Annotated Parallelism for Python on GPUs

Tong Zhou, Jun Shirako, Vivek Sarkar

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Tags: Code generation, Compilers, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce RTX 3090, Python

February 25, 2024 by hgpu

Analyzing GPU Performance in Virtualized Environments: A Case Study

Adel Belkhiri, Michel Dagenais

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Tags: Computer science, CUDA, Java, nVidia, OpenCL, Package, Performance, Virtualization

February 25, 2024 by hgpu

Green AI: A Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures

Negar Alizadeh, Fernando Castor

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Tags: Computer science, CUDA, Deep learning, Energy-efficient computing, nVidia, nVidia GeForce RTX 3070, Package, PyTorch, TensorFlow

February 25, 2024 by hgpu

Benchmarking and Dissecting the Nvidia Hopper GPU Architecture

Weile Luo, Ruibo Fan, Zeyu Li, Dayou Du, Qiang Wang, Xiaowen Chu

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Tags: Artificial intelligence, Benchmarking, Computer science, CUDA, Deep learning, nVidia, nVidia A100, nVidia GeForce RTX 4090, nVidia H800, Performance, PTX

February 25, 2024 by hgpu

pSTL-Bench: A Micro-Benchmark Suite for Assessing Scalability of C++ Parallel STL Implementations

Ruben Laso, Diego Krupitza, Sascha Hunold

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia Ampere A2, OpenMP, performance portability, Tesla P4

February 18, 2024 by hgpu

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

GigaAPI for GPU Parallelization

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

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high performance computing on graphics processing units: hgpu.org

FTTN: Feature-Targeted Testing for Numerical Properties of NVIDIA & AMD Matrix Accelerators

SYCL-Bench 2020: Benchmarking SYCL 2020 on AMD, Intel, and NVIDIA GPUs

Parallel Implementation of Lightweight Secure Hash Algorithm on CPU and GPU Environments

Sustainable Supercomputing for AI: GPU Power Capping at HPC Scale

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

Parallel programming in mobile devices with FancyJCL

Low-Overhead Trace Collection and Profiling on GPU Compute Kernels

APPy: Annotated Parallelism for Python on GPUs

Analyzing GPU Performance in Virtualized Environments: A Case Study

Green AI: A Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures

Benchmarking and Dissecting the Nvidia Hopper GPU Architecture

pSTL-Bench: A Micro-Benchmark Suite for Assessing Scalability of C++ Parallel STL Implementations

Recent source codes

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

DuoReduce: MLIR's benchmark

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