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HIPRT: A Ray Tracing Framework in HIP

Daniel Meister, Paritosh Kulkarni, Aaryaman Vasishta, Takahiro Harada

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Tags: 3D Graphics and Realism, AMD Radeon Pro W7900, ATI, HIP, Package, Raytracing, Rendering, Vulkan

August 14, 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

A Comprehensive Deep Learning Library Benchmark and Optimal Library Selection

Qiyang Zhang, Xiangying Che, Yijie Chen, Xiao Ma, Mengwei Xu, Schahram Dustdar, Xuanzhe Liu, Shangguang Wang

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Tags: Benchmarking, Computer science, Deep learning, Heterogeneous systems, Package

August 14, 2024 by hgpu

Acceleration for the many, not the few

Jackson Woodruff

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Tags: Compilers, Computer science, Heterogeneous systems, Package, Thesis

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

Lectures on Parallel Computing

Jesper Larsson Träff

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Tags: Computer science, Education, parallel 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

Data-driven Performance Optimization for Data-intensive Applications

Jie Liu

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Tags: Computer science, Databases, Deep learning, Neural networks, nVidia, nVidia V100, TensorFlow, Thesis

July 28, 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

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

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

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

Applications

HIPRT: A Ray Tracing Framework in HIP

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

A Comprehensive Deep Learning Library Benchmark and Optimal Library Selection

Acceleration for the many, not the few

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

Lectures on Parallel Computing

Data-driven Forecasting of Deep Learning Performance on GPUs

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

Data-driven Performance Optimization for Data-intensive Applications

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

Recent source codes

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Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

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

EnergyUCB-Bandit

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