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cuSZp2: A GPU Lossy Compressor with Extreme Throughput and Optimized Compression Ratio

Yafan Huang, Sheng Di, Guanpeng Li, Franck Cappello

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Tags: Compression, Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce RTX 3080, nVidia GeForce RTX 3090, Package, PTX

February 16, 2025 by hgpu

GSParLib: A multi-level programming interface unifying OpenCL and CUDA for expressing stream and data parallelism

Dinei A. Rockenbach, Gabriell Araujo, Dalvan Griebler, Luiz Gustavo Fernandes

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Tags: Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce RTX 3090, OpenCL, OpenMP, Package

January 20, 2025 by hgpu

Analyzing the Performance Portability of SYCL across CPUs, GPUs, and Hybrid Systems with Protein Database Search

Manuel Costanzo, Enzo Rucci, Carlos García-Sánchez, Marcelo Naiouf, Manuel Prieto-Matías

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Tags: AMD Radeon RX 6700 XT, AMD Radeon RX Vega 6, ATI, Bioinformatics, Biology, Computer science, CUDA, Databases, Heterogeneous systems, HPC, Intel, Intel Arc A770, Intel UHD 630, Intel UHD 770, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, nVidia GeForce RTX 2070, nVidia GeForce RTX 3070, nVidia GeForce RTX 3090, oneAPI, Package, performance portability, SYCL, Tesla V100

December 15, 2024 by hgpu

Over-synchronization in GPU Programs

Ajay Nayak, Arkaprava Basu

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Tags: Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce RTX 3090, Package, Performance, PTX

November 10, 2024 by hgpu

General-Purpose Computing on Tensor Processors

Kuan-Chieh Hsu

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Tags: Artificial intelligence, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce RTX 2080, nVidia GeForce RTX 3090, nVidia Jetson Nano, Package, Sparse matrix, Thesis, TPU

October 27, 2024 by hgpu

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

Reza Abbasi, Sernam Lim

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Tags: AI, Artificial intelligence, Computer science, CUDA, LLM, Machine learning, Memory, nVidia, nVidia GeForce RTX 3090, nVidia Quadro RTX 8000, Package

October 20, 2024 by hgpu

Efficient Arbitrary Precision Acceleration for Large Language Models on GPU Tensor Cores

Shaobo Ma, Chao Fang, Haikuo Shao, Zhongfeng Wang

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Tags: Computer science, CUDA, Data recovery, Extended precision, LLM, Matrix multiplication, nVidia, nVidia GeForce RTX 3090, Security

October 6, 2024 by hgpu

Double-Precision Floating-Point Data Visualizations Using Vulkan API

Nezihe Sözen

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Tags: Computer science, Extended precision, GLSL, nVidia, nVidia GeForce RTX 3090, Package, Rendering, Visualization, Vulkan

August 25, 2024 by hgpu

GRAFX: An Open-Source Library for Audio Processing Graphs in PyTorch

Sungho Lee, Marco Martínez-Ramírez, Wei-Hsiang Liao, Stefan Uhlich, Giorgio Fabbro, Kyogu Lee, Yuki Mitsufuji

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Tags: nVidia, nVidia GeForce RTX 3090, Package, PyTorch, Signal processing

August 18, 2024 by hgpu

Evaluating Operators in Deep Neural Networks for Improving Performance Portability of SYCL

Zheming Jin

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Tags: Benchmarking, Computer science, CUDA, HIP, Machine learning, Neural networks, nVidia, nVidia GeForce RTX 2080, nVidia GeForce RTX 3090, nVidia H100, oneAPI, Performance, performance portability, SYCL, Tesla A100, Tesla V100

August 14, 2024 by hgpu

A methodology for comparing optimization algorithms for auto-tuning

Floris-Jan Willemsen, Richard Schoonhoven, Jiří Filipovič, Jacob O. Tørring, Rob van Nieuwpoort, Ben van Werkhoven

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Tags: Computer science, CUDA, nVidia, nVidia GeForce RTX 2080 Ti, nVidia GeForce RTX 3090, Package, Performance

June 16, 2024 by hgpu

Addressing Challenges in Utilizing GPUs for Accelerating Privacy-Preserving Computation

Ardhi Wiratama Baskara Yudha

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia GeForce RTX 2080, nVidia GeForce RTX 3090, Security, Thesis

June 2, 2024 by hgpu

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

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

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high performance computing on graphics processing units: hgpu.org

cuSZp2: A GPU Lossy Compressor with Extreme Throughput and Optimized Compression Ratio

GSParLib: A multi-level programming interface unifying OpenCL and CUDA for expressing stream and data parallelism

Analyzing the Performance Portability of SYCL across CPUs, GPUs, and Hybrid Systems with Protein Database Search

Over-synchronization in GPU Programs

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

Efficient Arbitrary Precision Acceleration for Large Language Models on GPU Tensor Cores

Double-Precision Floating-Point Data Visualizations Using Vulkan API

GRAFX: An Open-Source Library for Audio Processing Graphs in PyTorch

Evaluating Operators in Deep Neural Networks for Improving Performance Portability of SYCL

A methodology for comparing optimization algorithms for auto-tuning

Addressing Challenges in Utilizing GPUs for Accelerating Privacy-Preserving Computation

Recent source codes

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

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

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