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TensorNetwork for Machine Learning

Stavros Efthymiou, Jack Hidary, Stefan Leichenauer

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Tags: Computer science, Deep learning, Machine learning, nVidia, Package, Tesla P100

June 20, 2019 by hgpu

Accelerating Concurrent Heap on GPUs

Yanhao Chen, Fei Hua, Chaozhang Huang, Jeremy Bierema, Chi Zhang, Eddy Z. Zhang

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce GTX Titan X

June 20, 2019 by hgpu

High-Performance Deep Learning via a Single Building Block

Evangelos Georganas, Kunal Banerjee, Dhiraj Kalamkar, Sasikanth Avancha, Anand Venkat, Michael Anderson, Greg Henry, Hans Pabst, Alexander Heinecke

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Tags: Computer science, Deep learning, LSTM, Machine learning, Neural networks, OpenCL, Performance

June 20, 2019 by hgpu

Performance Evaluation and Analysis of Sparse Matrix and Graph Kernels on Heterogeneous Processors

Feng Zhang, Weifeng Liu, Ningxuan Feng, Jidong Zhai, Xiaoyong Du

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Tags: ATI, Computer science, Heterogeneous systems, OpenCL, Sparse matrix

June 16, 2019 by hgpu

RTX Beyond Ray Tracing: Exploring the Use of Hardware Ray Tracing Cores for Tet-Mesh Point Location

Ingo Wald, Will Usher, Nate Morrical, Laura Lediaev, Valerio Pascucci

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Tags: 3D Graphics and Realism, CUDA, nVidia, nVidia Titan RTX, Raytracing

June 16, 2019 by hgpu

SYCL Code Generation for Multigrid Methods

Stefan Groth, Christian Schmitt, Jürgen Teich, and Frank Hannig

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Tags: Code generation, Computer science, Differential equations, OpenCL, Package, Partial differential equations, PDEs

June 16, 2019 by hgpu

Software Compilation Techniques for Heterogeneous Embedded Multi-Core Systems

Rainer Leupers, Miguel Angel Aguilar, Jeronimo Castrillon, Weihua Sheng

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Tags: ARM, Code generation, Compilers, Computer science, DSP, Heterogeneous systems

June 16, 2019 by hgpu

Performance Analysis and Automatic Tuning of Hash Aggregation on GPUs

Viktor Rosenfeld, Sebastian Bress, Steffen Zeuch, Tilmann Rabl, Volker Markl

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Tags: Algorithms, AMD Radeon R9 Fury, ATI, Computer science, Hashing, nVidia, nVidia GeForce GXT 1080, nVidia GeForce GXT 980, OpenCL, Performance, Tesla K40, Tesla V100

June 16, 2019 by hgpu

Performance Modelling of Deep Learning on Intel Many Integrated Core Architectures

Andre Viebke, Sabri Pllana, Suejb Memeti, Joanna Kolodziej

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Tags: Computer science, Deep learning, Intel Xeon Phi, Neural networks, OpenMP, Performance

June 12, 2019 by hgpu

Parallel scalable simulations of biological neural networks using TensorFlow: A beginner’s guide

Saptarshi Soham Mohanta, Collins Assisi

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Tags: Biology, Computer science, CUDA, Deep learning, Differential equations, Machine learning, Neural networks, nVidia, ODEs, Ordinary differential equations, Package, Partial differential equations, PDEs, Python, TensorFlow, Tutorial

June 12, 2019 by hgpu

A Survey on Evaluating and Optimizing Performance of Intel Xeon Phi

Sparsh Mittal, Osho Anand, Visnu P Kumarr

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Tags: Compilers, Computer science, Intel Xeon Phi, OpenMP, survey

June 9, 2019 by hgpu

PPOpenCL: a performance-portable OpenCL compiler with host and kernel thread code fusion

Ying Liu, Lei Huang, Mingchuan Wu, Huimin Cui, Fang Lv, Xiaobing Feng, Jingling Xue

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Tags: Code generation, Compilers, Computer science, OpenCL

June 9, 2019 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

Applications

TensorNetwork for Machine Learning

Accelerating Concurrent Heap on GPUs

High-Performance Deep Learning via a Single Building Block

Performance Evaluation and Analysis of Sparse Matrix and Graph Kernels on Heterogeneous Processors

RTX Beyond Ray Tracing: Exploring the Use of Hardware Ray Tracing Cores for Tet-Mesh Point Location

SYCL Code Generation for Multigrid Methods

Software Compilation Techniques for Heterogeneous Embedded Multi-Core Systems

Performance Analysis and Automatic Tuning of Hash Aggregation on GPUs

Performance Modelling of Deep Learning on Intel Many Integrated Core Architectures

Parallel scalable simulations of biological neural networks using TensorFlow: A beginner’s guide

A Survey on Evaluating and Optimizing Performance of Intel Xeon Phi

PPOpenCL: a performance-portable OpenCL compiler with host and kernel thread code fusion

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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