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Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations

Philippe Tillet, H. T. Kung, David Cox

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Tags: Compilers, Computer science, CUDA, Deep learning, High-level Languages, LLVM, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX 1070, Package

June 30, 2019 by hgpu

IA-SpGEMM: An Input-aware Auto-tuning Framework for Parallel Sparse Matrix-Matrix Multiplication

Zhen Xie, Guangming Tan, Weifeng Liu, Ninghui Sun

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Tags: Algorithms, Auto-Tuning, Computer science, CUDA, Deep learning, Matrix multiplication, nVidia, Sparse matrix, Tesla P100

June 23, 2019 by hgpu

Performance Engineering for a Tall & Skinny Matrix Multiplication Kernel on GPUs

Dominik Ernst, Georg Hager, Jonas Thies, Gerhard Wellein

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Tags: Benchmarking, Code generation, Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, Package, Performance, Tesla V100

May 12, 2019 by hgpu

A mechanism for balancing accuracy and scope in cross-machine black-box GPU performance modeling

James D. Stevens

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Tags: AMD Radeon R9 Fury, ATI, Code generation, Computer science, Finite difference, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX Titan X, OpenCL, Performance, Tesla C2070, Tesla K40

April 28, 2019 by hgpu

Novel Data-Partitioning Algorithms for Performance and Energy Optimization of Data-Parallel Applications on Modern Heterogeneous HPC Platforms

Hamidreza Khaleghzadeh

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, Matrix multiplication, nVidia, OpenCL, Package, Thesis

March 17, 2019 by hgpu

Supporting mixed-datatype matrix multiplication within the BLIS framework

Field G. Van Zee, Devangi N. Parikh, Robert A. van de Geijn

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Tags: Computer science, Matrix multiplication, OpenMP, Package

January 27, 2019 by hgpu

Performance Evaluation and Tuning of An OpenCL based Matrix Multiplier

Yiyu Tan, Toshiyuki Imamura

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Tags: Computer science, FPGA, Linear Algebra, Matrix multiplication, OpenCL

September 2, 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

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

Implementing general matrix-matrix multiplication algorithm on the Intel Xeon Phi Knights Landing Processor

Raehyun Kim

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Tags: Algorithms, Computer science, Intel Xeon Phi, Linear Algebra, Matrix multiplication, Optimization

June 13, 2018 by hgpu

Learning to Optimize Tensor Programs

Tianqi Chen, Lianmin Zheng, Eddie Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

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Tags: ARM, Computer science, CUDA, Deep learning, Matrix multiplication, nVidia, nVidia GeForce GTX Titan X

May 26, 2018 by hgpu

High-performance sparse matrix-matrix products on Intel KNL and multicore architectures

Yusuke Nagasaka, Satoshi Matsuoka, Ariful Azad, Aydin Buluc

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Tags: Algorithms, Computer science, Intel Xeon Phi, Matrix multiplication, Sparse matrix

April 7, 2018 by hgpu

A Versatile Software Systolic Execution Model for GPU Memory-Bound Kernels

A Versatile Software Systolic Execution Model for GPU Memory-Bound Kernels

blockgmf: Block based approaches to Matrix Factorization

Block based Singular Value Decomposition approach to matrix factorization for recommender systems

CU2CL: A prototype CUDA-to-OpenCL source-to-source translator

On the Portability of CPU-Accelerated Applications via Automated Source-to-Source Translation

GPU-based Parallel Computation Support for Stan

GPU-based Parallel Computation Support for Stan

PANNA: Properties from Artificial Neural Network Architectures

PANNA: Properties from Artificial Neural Network Architectures

Artifact Evaluation for Novel methodologies for predictable CPU-to-GPU command offloading

Novel Methodologies for Predictable CPU-To-GPU Command Offloading

tvm: Open deep learning compiler stack for cpu, gpu and specialized accelerators

A Unified Optimization Approach for CNN Model Inference on Integrated GPUs

picongpu: Particle-in-Cell Simulations for the Exascale Era

PIConGPU: Predictive Simulations of Laser-Particle Accelerators with Manycore Hardware

WCCV: Warp-coherent Condition Vectorization(on top of RV)

WCCV: Improving the Vectorization of IF-statements with Warp-Coherent Conditions

Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations

Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations

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2019

May

13-15

Boston, USA

7th International Workshop on OpenCL, 2019

2019

May

13

Boston, MA, USA

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

2019

May

14

Larnaca, Cyprus

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Neural Networks (ICFNN), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML), 2019

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