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Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

Jack Turner, Jose Cano, Valentin Radu, Elliot J. Crowley, Michael O'Boyle, Amos Storkey

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Tags: ARM, Compression, Computer science, Deep learning, Machine learning, Neural networks, OpenCL, OpenMP, Package

September 16, 2018 by hgpu

ZUCL: A ZYNQ UltraScale+ Framework for OpenCL HLS Applications

Khoa Dang Pham, Anuj Vaishnav, Malte Vesper, Dirk Koch

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Tags: Computer science, FPGA, HLS, OpenCL, Package, Task scheduling

September 16, 2018 by hgpu

Efficient and Scalable k-Means on GPUs

Clemens Lutz, Sebastian Bress, Tilmann Rabl, Steffen Zeuch, Volker Markl

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Tags: Algorithms, Clustering, Computer science, nVidia, nVidia GeForce GTX 1080, OpenCL, Package

September 9, 2018 by hgpu

Implementing Strassen’s Algorithm with CUTLASS on NVIDIA Volta GPUs

Jianyu Huang, Chenhan D. Yu, Robert A. van de Geijn

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, Package, Tesla V100

September 2, 2018 by hgpu

A study of integer sorting on multicores

Alexandros V. Gerbessiotis

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Tags: Algorithms, Computer science, MPI, Package, Performance, Sorting

September 2, 2018 by hgpu

Kernel Tuner: A search-optimizing GPU code auto-tuner

Ben van Werkhoven

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Tags: AMD Radeon Vega Frontier Edition, ATI, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, OpenCL, Optimization, Package

August 19, 2018 by hgpu

Anatomy Of High-Performance Deep Learning Convolutions On SIMD Architectures

Evangelos Georganas, Sasikanth Avancha, Kunal Banerjee, Dhiraj Kalamkar, Greg Henry, Hans Pabst, Alexander Heinecke

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

August 19, 2018 by hgpu

Matrix Factorization on GPUs with Memory Optimization and Approximate Computing

Wei Tan, Shiyu Chang, Liana Fong, Cheng Li, Zijun Wang, Liangliang Cao

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Tags: Algorithms, Compression, Computer science, CUDA, Factorization, nVidia, nVidia GeForce GTX Titan X, Package

August 19, 2018 by hgpu

libhclooc: Software Library Facilitating Out-of-core Implementations of Accelerator Kernels on Hybrid Computing Platforms

Daniel Hanlon, Hamidreza Khalighzadeh, Ravi Reddy Manumachu, Alexey Lastovetsky

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Tags: Cloud, Computer science, CUDA, FPGA, Hybrid computing, Intel Xeon Phi, Matrix multiplication, nVidia, OpenCL, Package

August 19, 2018 by hgpu

Large Scale Language Modeling: Converging on 40GB of Text in Four Hours

Raul Puri, Robert Kirby, Nikolai Yakovenko, Bryan Catanzaro

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Tags: Computer science, CUDA, Deep learning, LSTM, Mixed precision, Neural networks, nVidia, Package, Tesla V100, Torch

August 11, 2018 by hgpu

Parallax: Automatic Data-Parallel Training of Deep Neural Networks

Soojeong Kim, Gyeong-In Yu, Hojin Park, Sungwoo Cho, Eunji Jeong, Hyeonmin Ha, Sanha Lee, Joo Seong Jeong, Byung-Gon Chun

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan XP, OpenMPI, Package

August 11, 2018 by hgpu

Spatial: A Language and Compiler for Application Accelerators

David Koeplinger, Matthew Feldman, Raghu Prabhakar, Yaqi Zhang, Stefan Hadjis, Ruben Fiszel, Tian Zhao, Luigi Nardi, Ardavan Pedram, Christos Kozyrakis, Kunle Olukotun

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Tags: Compilers, Computer science, Deep learning, DSL, FPGA, HLS, Machine learning, Package, Scala

July 21, 2018 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

Packages

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

ZUCL: A ZYNQ UltraScale+ Framework for OpenCL HLS Applications

Efficient and Scalable k-Means on GPUs

Implementing Strassen’s Algorithm with CUTLASS on NVIDIA Volta GPUs

A study of integer sorting on multicores

Kernel Tuner: A search-optimizing GPU code auto-tuner

Anatomy Of High-Performance Deep Learning Convolutions On SIMD Architectures

Matrix Factorization on GPUs with Memory Optimization and Approximate Computing

Large Scale Language Modeling: Converging on 40GB of Text in Four Hours

Parallax: Automatic Data-Parallel Training of Deep Neural Networks

Spatial: A Language and Compiler for Application Accelerators

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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