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Dynamically Finding Optimal Kernel Launch Parameters for CUDA Programs

Taabish Jeshani

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce RTX 2070, Package, Performance, Thesis

May 7, 2023 by hgpu

Redwood: Flexible and Portable Heterogeneous Tree Traversal Workloads

Yanwen Xu, Ang Li, Tyler Sorensen

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Tags: Benchmarking, Computer science, CUDA, FPGA, Heterogeneous systems, HLS, Intel UHD 630, nVidia, nVidia Jetson AGX Xavier, nVidia Jetson Nano, Package, Performance, SYCL

May 7, 2023 by hgpu

Anatomy of High-Performance GEMM with Online Fault Tolerance on GPUs

Shixun Wu, Yujia Zhai, Jinyang Liu, Jiajun Huang, Zizhe Jian, Bryan M. Wong, Zizhong Chen

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Tags: Code generation, Computer science, CUDA, GEMM, Linear Algebra, Matrix multiplication, nVidia, nVidia A100, Package, Performance, Reliability, Tesla T4

May 7, 2023 by hgpu

FZ-GPU: A Fast and High-Ratio Lossy Compressor for Scientific Computing Applications on GPUs

Boyuan Zhang, Jiannan Tian, Sheng Di, Xiaodong Yu, Yunhe Feng, Xin Liang, Dingwen Tao, Franck Cappello

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, nVidia A100, nVidia RTX A4000, Package, Signal processing

May 7, 2023 by hgpu

Simple and efficient GPU accelerated topology optimisation: Codes and applications

Erik A. Träff, Anton Rydahl, Sven Karlsson, Ole Sigmund, Niels Aage

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Tags: Computer science, CUDA, nVidia, nVidia A100, OpenCL, OpenMP, Package, Topology optimization problem

April 23, 2023 by hgpu

Thread-safe lattice Boltzmann for high-performance computing on GPUs

Andrea Montessori, Marco Lauricella, Adriano Tiribocchi, Mihir Durve, Michele La Rocca, Giorgio Amati, Fabio Bonaccorso, Sauro Succi

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Tags: Algorithms, cfd, CUDA, Fluid dynamics, Lattice Boltzmann model, nVidia, nVidia A100, Package

April 23, 2023 by hgpu

GPULZ: Optimizing LZSS Lossless Compression for Multi-byte Data on Modern GPUs

Boyuan Zhang, Jiannan Tian, Sheng Di, Xiaodong Yu, Martin Swany, Dingwen Tao, Franck Cappello

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, nVidia A100, nVidia RTX A4000, Package

April 23, 2023 by hgpu

Kernel Tuning Toolkit

Filip Petrovič, Jiří Filipovič

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 1070, nVidia GeForce GTX 680, nVidia GeForce GTX 750, nVidia GeForce RTX 2080 Ti, OpenCL, Package, Performance, Python, Vulkan

April 16, 2023 by hgpu

Understanding Performance Portability of Bioinformatics Applications in SYCL on an NVIDIA GPU

Zheming Jin, Jeffrey S. Vetter

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Tags: Benchmarking, Bioinformatics, Computer science, CUDA, Heterogeneous systems, nVidia, Package, performance portability, SYCL, Tesla V100

April 16, 2023 by hgpu

Task parallelism-based architectures on FPGA to optimize the energy efficiency of AI at the edge

Rafael Gadea-Gironés, Jorge Fe, Jose M. Monzo

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Tags: Artificial intelligence, Computer science, Energy-efficient computing, FPGA, Neural networks, OpenCL

April 2, 2023 by hgpu

Managing heterogeneous device memory using C++17 memory resources

S.N. Swatman, A. Krasznahorkay, P. Gessinger

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Tags: Computer science, CUDA, Heterogeneous systems, HIP, Memory model, nVidia, Package, SYCL

April 2, 2023 by hgpu

PopSparse: Accelerated block sparse matrix multiplication on IPU

Zhiyi Li, Douglas Orr, Valeriu Ohan, Godfrey Da costa, Tom Murray, Adam Sanders, Deniz Beker, Dominic Masters

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Tags: Computer science, CUDA, Deep learning, Linear Algebra, Machine learning, Matrix multiplication, nVidia, nVidia A100, Package, Sparse matrix

April 2, 2023 by hgpu

HipKittens: Fast and Furious AMD Kernels

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

LC Framework

Characterizing the Performance of Parallel Data-Compression Algorithms across Compilers and GPUs

pplx-garden: Perplexity open source garden for inference technology

RDMA Point-to-Point Communication for LLM Systems

OpScanner

A Study of Floating-Point Precision Tuning in Deep Learning Operators Implementations

Atlas CLI: Machine Learning (ML) Lifecycle & Transparency Manager

Scalable GPU-Based Integrity Verification for Large Machine Learning Models

transformers_tvm: Implementation of Encoder Decoder transformer on TVM

Enhancing Transformer Performance and Portability through Auto-tuning Frameworks

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high performance computing on graphics processing units: hgpu.org

Programming

Dynamically Finding Optimal Kernel Launch Parameters for CUDA Programs

Redwood: Flexible and Portable Heterogeneous Tree Traversal Workloads

Anatomy of High-Performance GEMM with Online Fault Tolerance on GPUs

FZ-GPU: A Fast and High-Ratio Lossy Compressor for Scientific Computing Applications on GPUs

Simple and efficient GPU accelerated topology optimisation: Codes and applications

Thread-safe lattice Boltzmann for high-performance computing on GPUs

GPULZ: Optimizing LZSS Lossless Compression for Multi-byte Data on Modern GPUs

Kernel Tuning Toolkit

Understanding Performance Portability of Bioinformatics Applications in SYCL on an NVIDIA GPU

Task parallelism-based architectures on FPGA to optimize the energy efficiency of AI at the edge

Managing heterogeneous device memory using C++17 memory resources

PopSparse: Accelerated block sparse matrix multiplication on IPU

Recent source codes

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

LC Framework

pplx-garden: Perplexity open source garden for inference technology

OpScanner

Atlas CLI: Machine Learning (ML) Lifecycle & Transparency Manager

transformers_tvm: Implementation of Encoder Decoder transformer on TVM

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