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Characterizing Deep Learning Training Workloads on Alibaba-PAI

Mengdi Wang, Chen Meng, Guoping Long, Chuan Wu, Jun Yang, Wei Lin, Yangqing Jia

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Tags: Artificial intelligence, Computer science, Computer vision, Deep learning, GPU cluster, nVidia, Tesla V100

October 20, 2019 by hgpu

The Memory Controller Wall: Benchmarking the Intel FPGA SDK for OpenCL Memory Interface

Hamid Reza Zohouri, Satoshi Matsuoka

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Tags: Benchmarking, Computer science, FPGA, HLS, Memory, nVidia, OpenCL, Package, Tesla K20, Tesla V100

October 20, 2019 by hgpu

Accelerated Approximate Nearest Neighbors Search Through Hierarchical Product Quantization

Ameer M.S. Abdelhadi, Christos-Savvas Bouganis, George A. Constantinides

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Tags: Computer science, FPGA, Information Retrieval, Machine learning, Nearest neighbour, Package

October 13, 2019 by hgpu

Performance Evaluation of Blocking and NonBlocking Concurrent Queues on GPUs

Hossein Pourmeidani

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Tags: AMD Radeon R7, ATI, Benchmarking, Computer science, OpenCL, Performance, Thesis

October 13, 2019 by hgpu

Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs

Valentin Radu, Kuba Kaszyk, Yuan Wen, Jack Turner, Jose Cano, Elliot J. Crowley, Bjorn Franke, Amos Storkey, Michael O'Boyle

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Tags: Computer science, CUDA, Neural networks, nVidia, nVidia Jetson TX2, OpenCL

October 13, 2019 by hgpu

hlslib: Software Engineering for Hardware Design

Johannes de Fine Licht, Torsten Hoefler

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Tags: Computer science, FPGA, OpenCL, Package, Software Engineering

October 13, 2019 by hgpu

Performance Impact of Memory Channels on Sparse and Irregular Algorithms

Oded Green, James Fox, Jeffrey Young, Jun Shirako, David Bader

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, nVidia GeForce GTX Titan XP, Performance, Sparse, Tesla P100, Tesla V100

October 13, 2019 by hgpu

Taichi: A Language for High-Performance Computation on Spatially Sparse Data Structures

Yuanming Hu, Tzu-Mao Li, Luke Anderson, Jonathan Ragan-Kelley, Frédo Durand

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Tags: 3D Graphics and Realism, CUDA, High-level Languages, nVidia, nVidia GeForce GTX 1080 Ti, Package, Rendering, Tesla V100

October 6, 2019 by hgpu

Verification of GPU Program Optimizations in Lean

Björn Fischer

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Tags: Computer science, High-level Languages, Package, Thesis

October 6, 2019 by hgpu

Syntix: A Profiling Based Resource Estimator for CUDA Kernels

Maria Papadaki, Yannis Sfakianakis, Christos Kozanitis, Angelos Bilas

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Tags: Computer science, CUDA, GPU cluster, Grid, nVidia, nVidia Quadro K 2200

October 6, 2019 by hgpu

MIOpen: An Open Source Library For Deep Learning Primitives

Jehandad Khan, Paul Fultz, Artem Tamazov, Daniel Lowell, Chao Liu, Michael Melesse, Murali Nandhimandalam, Kamil Nasyrov, Ilya Perminov, Tejash Shah, Vasilii Filippov, Jing Zhang, Jing Zhou, Bragadeesh Natarajan, Mayank Daga

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Tags: Computer science, Deep learning, Neural networks, OpenCL, Package

October 6, 2019 by hgpu

Exascale Deep Learning for Scientific Inverse Problems

Nouamane Laanait, Joshua Romero, Junqi Yin, M. Todd Young, Sean Treichler, Vitalii Starchenko, Albina Borisevich, Alex Sergeev, Michael Matheson

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Tags: Computer science, Deep learning, Neural networks, nVidia, Package, Tesla V100

September 29, 2019 by hgpu

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

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

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

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high performance computing on graphics processing units: hgpu.org

Applications

Characterizing Deep Learning Training Workloads on Alibaba-PAI

The Memory Controller Wall: Benchmarking the Intel FPGA SDK for OpenCL Memory Interface

Accelerated Approximate Nearest Neighbors Search Through Hierarchical Product Quantization

Performance Evaluation of Blocking and NonBlocking Concurrent Queues on GPUs

Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs

hlslib: Software Engineering for Hardware Design

Performance Impact of Memory Channels on Sparse and Irregular Algorithms

Taichi: A Language for High-Performance Computation on Spatially Sparse Data Structures

Verification of GPU Program Optimizations in Lean

Syntix: A Profiling Based Resource Estimator for CUDA Kernels

MIOpen: An Open Source Library For Deep Learning Primitives

Exascale Deep Learning for Scientific Inverse Problems

Recent source codes

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

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

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