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Confidential Computing on Heterogeneous Systems: Survey and Implications

Qifan Wang, David Oswald

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, nVidia H100, Review, Security

August 25, 2024 by hgpu

Characterizing CUDA and OpenMP Synchronization Primitives

Brandon Alexander Burtchell, Martin Burtscher

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Tags: Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce RTX 2070, nVidia GeForce RTX 4090, OpenMP, Package, Performance

August 25, 2024 by hgpu

The VerCors Verifier: A Progress Report

Lukas Armborst, Pieter Bos, Lars B. van den Haak, Marieke Huisman, Robert Rubbens, Ömer Şakar, Philip Tasche

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Tags: Computer science, CUDA, OpenCL, OpenMP, Package, Performance, SYCL

August 18, 2024 by hgpu

HiCCL: A Hierarchical Collective Communication Library

Mert Hidayetoglu, Simon Garcia de Gonzalo, Elliott Slaughter, Pinku Surana, Wen-mei Hwu, William Gropp, Alex Aiken

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Tags: AMD Radeon Instinct MI250X, ATI, Computer science, CUDA, HIP, nVidia, nVidia A100, Package, Performance, SYCL

August 18, 2024 by hgpu

Anatomizing Deep Learning Inference in Web Browsers

Qipeng Wang, Shiqi Jiang, Zhenpeng Chen, Xu Cao, Yuanchun Li, Aoyu Li, Yun Ma, Ting Cao, Xuanzhe Liu

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1060, nVidia GeForce GTX 980, nVidia GeForce RTX 2060, OpenCL, Package, Performance, TensorFlow

August 18, 2024 by hgpu

In-Situ Techniques on GPU-Accelerated Data-Intensive Applications

Yi Ju, Mingshuai Li, Adalberto Perez, Laura Bellentani, Niclas Jansson, Stefano Markidis, Philipp Schlatter, Erwin Laure

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Tags: Computer science, CUDA, Fluid dynamics, HPC, Molecular dynamics, nVidia, nVidia A100, PC cluster, Performance

August 14, 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

LO-SpMM: Low-cost Search for High-performance SpMM Kernels on GPUs

Junqing Lin, Jingwei Sun, Xiaolong Shi, Honghe Zhang, Xianzhi Yu, Xinzhi Wang, Jun Yao, Guangzhong Sun

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Tags: Compilers, Computer science, CUDA, Deep learning, Linear Algebra, Matrix multiplication, Neural networks, nVidia, nVidia GeForce RTX 2080 Ti, Performance, Sparse matrix, Tesla V100

August 4, 2024 by hgpu

Springald: GPU-Accelerated Window-Based Aggregates Over Out-of-Order Data Streams

Gabriele Mencagli, Patrizio Dazzi, Massimo Coppola

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Tags: Computer science, CUDA, DSP, nVidia, nVidia A30, Package, Stream Computing

August 4, 2024 by hgpu

Data-driven Forecasting of Deep Learning Performance on GPUs

Seonho Lee, Amar Phanishayee, Divya Mahajan

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia A100, nVidia H100, nVidia P100, nVidia V100, Performance, PyTorch, Tesla T4

August 4, 2024 by hgpu

Scheduling Deep Learning Jobs in Multi-Tenant GPU Clusters via Wise Resource Sharing

Yizhou Luo, Qiang Wang, Shaohuai Shi, Jiaxin Lai, Shuhan Qi, Jiajia Zhang, Xuan Wang

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Tags: Computer science, CUDA, Deep learning, GPU cluster, nVidia, nVidia GeForce RTX 2080 Ti, OpenMPI, PyTorch, Task scheduling

August 4, 2024 by hgpu

A Comparison of OpenCL, CUDA, and HIP as Compilation Targets for a Functional Array Language

Troels Henriksen

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Tags: AMD Radeon Instinct MI100, ATI, Code generation, Computer science, CUDA, HIP, nVidia, nVidia A100, OpenCL, Package, Programming Languages

July 28, 2024 by hgpu

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

high performance computing on graphics processing units: hgpu.org

Programming

Confidential Computing on Heterogeneous Systems: Survey and Implications

Characterizing CUDA and OpenMP Synchronization Primitives

The VerCors Verifier: A Progress Report

HiCCL: A Hierarchical Collective Communication Library

Anatomizing Deep Learning Inference in Web Browsers

In-Situ Techniques on GPU-Accelerated Data-Intensive Applications

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

LO-SpMM: Low-cost Search for High-performance SpMM Kernels on GPUs

Springald: GPU-Accelerated Window-Based Aggregates Over Out-of-Order Data Streams

Data-driven Forecasting of Deep Learning Performance on GPUs

Scheduling Deep Learning Jobs in Multi-Tenant GPU Clusters via Wise Resource Sharing

A Comparison of OpenCL, CUDA, and HIP as Compilation Targets for a Functional Array Language

Recent source codes

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

Shamrock: Multi-GPU hydrodynamics for astrophysics

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