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OpenCL Actors – Adding Data Parallelism to Actor-based Programming with CAF

Raphael Hiesgen, Dominik Charousset, Thomas C. Schmidt

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Tags: Computer science, Data parallelism, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 780 M, OpenCL, Package, Tesla C2075

September 28, 2017 by hgpu

Mixed Precision Solver Scalable to 16000 MPI Processes for Lattice Quantum Chromodynamics Simulations on the Oakforest-PACS System

Taisuke Boku, Ishikawa Ken-Ichi, Yoshinobu Kuramashi, Lawrence Meadows

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Tags: Computational Physics, High Energy Physics - Lattice, Intel Xeon Phi, Mixed precision, MPI, OpenMP, Package, Physics, QCD

September 28, 2017 by hgpu

Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices

Zongqing Lu, Swati Rallapalli, Kevin Chan, Thomas La Porta

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Tags: Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia Jetson TK1, nVidia Tegra TX1

September 28, 2017 by hgpu

GALARIO: a GPU Accelerated Library for Analysing Radio Interferometer Observations

Marco Tazzari, Frederik Beaujean, Leonardo Testi

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Tags: Astrophysics, CUDA, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce GTX 1060, Package, Python, Tesla P100

September 28, 2017 by hgpu

Accelerating Electron Tomography Reconstruction Algorithm ICON Using the Intel Xeon Phi Coprocessor on Tianhe-2 Supercomputer

Zihao Wang, Yu Chen, Jingrong Zhang, Lun Li, Xiaohua Wan, Zhiyong Liu, Fei Sun, Fa Zhang

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Tags: intel mic, Intel Xeon Phi, NUFFT, supercomputer, Tianhe-2

September 28, 2017 by holy

Asynchronous Task-Based Polar Decomposition on Single Node Manycore Architectures

Dalal Sukkari, Hatem Ltaief, Mathieu Faverge, David Keyes

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Tags: Algorithms, Benchmarking, Computer science, Factorization, Intel Xeon Phi, nVidia, StarPU, Task scheduling, Tesla K80, Tesla P100

September 21, 2017 by hgpu

Accelerating Radio Astronomy with Auto-Tuning

Alessio Sclocco

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Tags: AMD FirePro W9100, AMD Radeon R9 Fury X, Astrophysics, ATI, ATI Radeon HD 6970, Intel Xeon Phi, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 580, nVidia GeForce GTX 680, nVidia GeForce GTX Titan, nVidia GeForce GTX Titan X, OpenCL, Package, Tesla K20, Thesis

September 21, 2017 by hgpu

Automated Testing of Graphics Shader Compilers

Alastair F. Donaldson, Hugues Evrard, Andrei Lascu, Paul Thomson

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Tags: AMD Radeon R9 Fury, ARM, ATI, ATI Radeon HD 7700, Compilers, Computer science, GLSL, Intel, nVidia, nVidia GeForce GTX 770, nVidia GeForce GTX Titan X, OpenGL, Rendering, Security, Shaders, WebGL

September 21, 2017 by hgpu

IBM Deep Learning Service

Bishwaranjan Bhattacharjee, Scott Boag, Chandani Doshi, Parijat Dube, Ben Herta, Vatche Ishakian, K. R. Jayaram, Rania Khalaf, Avesh Krishna, Yu Bo Li, Vinod Muthusamy, Ruchir Puri, Yufei Ren, Florian Rosenberg, Seetharami R. Seelam, Yandong Wang, Jian Ming Zhang, Li Zhang

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Tags: Cloud, Computer science, CUDA, Deep learning, Heterogeneous systems, Machine learning, Neural networks, nVidia

September 21, 2017 by hgpu

Distributed Training Large-Scale Deep Architectures

Shang-Xuan Zou, Chun-Yen Chen, Jui-Lin Wu, Chun-Nan Chou, Chia-Chin Tsao, Kuan-Chieh Tung, Ting-Wei Lin, Cheng-Lung Sung, Edward Y. Chang

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Data parallelism, Deep learning, nVidia, Tesla K80

September 21, 2017 by hgpu

Out-of-core Implementation for Accelerator Kernels on Heterogeneous Clouds

Hamidreza Khaleghzadeh, Ziming Zhong, Ravi Reddy, Alexey Lastovetsky

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Tags: BLAS, Cloud, Computer science, CUBLAS, CUDA, FPGA, Heterogeneous systems, Intel Xeon Phi, Matrix multiplication, nVidia, OpenCL, Package, Virtualization

September 16, 2017 by hgpu

Meta Networks for Neural Style Transfer

Falong Shen, Shuicheng Yan, Gang Zeng

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Tags: Caffe, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX Titan X, Package

September 16, 2017 by hgpu

OpScanner

A Study of Floating-Point Precision Tuning in Deep Learning Operators Implementations

Atlas CLI: Machine Learning (ML) Lifecycle & Transparency Manager

Scalable GPU-Based Integrity Verification for Large Machine Learning Models

transformers_tvm: Implementation of Encoder Decoder transformer on TVM

Enhancing Transformer Performance and Portability through Auto-tuning Frameworks

INT v.s. FP: A framework to compare low-bit integer and float-point formats

INT v.s. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats

AutoDock-GPU: AutoDock for GPUs and other accelerators

Architecting Tensor Core-Based Reductions for Irregular Molecular Docking Kernels

NCCLX: collective communication framework

Collective Communication for 100k+ GPUs

Tutoring LLM into a Better CUDA Optimizer

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

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