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Serverless Computing Strategies on Cloud Platforms

Diana María Naranjo Delgado

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Tags: Cloud, Computer science, CUDA, Machine learning, nVidia, Tesla V100, Thesis, Virtualization

February 14, 2021 by hgpu

Optimization of Data Assignment for Parallel Processing in a Hybrid Heterogeneous Environment Using Integer Linear Programming

Tomasz Boiński, Paweł Czarnul

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Tags: Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 560 Ti, OpenCL, Tesla K20

February 14, 2021 by hgpu

Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures

Jiří Filipovič, Jana Hozzová, Amin Nezarat, Jaroslav Oľha, Filip Petrovič

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Tags: Auto-Tuning, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 1070, nVidia GeForce GTX 680, nVidia GeForce GTX 750, nVidia GeForce RTX 2080, Package

February 14, 2021 by hgpu

Transparent FPGA Acceleration with TensorFlow

Simon Pfenning, Philipp Holzinger, Marc Reichenbach

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Tags: Computer science, Deep learning, FPGA, Hardware Architecture, HSA, OpenCL, Package, TensorFlow

February 14, 2021 by hgpu

Dependable Embedded Systems

Jörg Henkel, Nikil Dutt (Editors)

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Tags: Book, Computer science, FPGA, OpenCL

February 7, 2021 by hgpu

triSYCL for Xilinx FPGA

Andrew Gozillon, Ronan Keryell, Lin-Ya Yu, Gauthier Harnisch, Paul Keir

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Tags: Computer science, FPGA, HPC, LLVM, Package, SYCL

February 7, 2021 by hgpu

Computational Performance Predictions for Deep Neural Network Training: A Runtime-Based Approach

Geoffrey X. Yu, Yubo Gao, Pavel Golikov, Gennady Pekhimenko

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce RTX 2070, Performance, Python, Tesla P100, Tesla V100

February 7, 2021 by hgpu

AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning

Yuhan Liu, Saurabh Agarwal, Shivaram Venkataraman

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Tags: Computer science, Deep learning, Machine learning, NLP, nVidia, nVidia A100, Package, Python, Tesla P100

February 7, 2021 by hgpu

Why is FPGA-GPU Heterogeneity the Best Option for Embedded Deep Neural Networks?

Walther Carballo-Hernández, Maxime Pelcat, François Berry

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Tags: Computer science, Deep learning, FPGA, Heterogeneous systems, Neural networks, nVidia, nVidia Jetson TX2, Performance

February 7, 2021 by hgpu

C-for-Metal: High Performance SIMD Programming on Intel GPUs

Guei-Yuan Lueh, Kaiyu Chen, Gang Chen, Joel Fuentes, Wei-Yu Chen, Fangwen Fu, Hong Jiang, Hongzheng Li, Daniel Rhee

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Tags: Computer science, CUDA, nVidia, OpenCL, Package, Programming Languages

January 31, 2021 by hgpu

Efficient MPI-based Communication for GPU-Accelerated Dask Applications

Aamir Shafi, Jahanzeb Maqbool Hashmi, Hari Subramoni, Dhabaleswar K. (DK) Panda

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Tags: Computer science, CUDA, Distributed computing, MPI, nVidia, Package, Performance, Python, Tesla V100

January 31, 2021 by hgpu

Text2Gestures: A Transformer-Based Network for Generating Emotive Body Gestures for Virtual Agents

Uttaran Bhattacharya, Nicholas Rewkowski, Abhishek Banerjee, Pooja Guhan, Aniket Bera, Dinesh Manocha

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Tags: Biomechanics, Computer science, Deep learning, nVidia, nVidia GeForce GTX 1080 Ti, Package, Virtual reality

January 31, 2021 by hgpu

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

PyOMP: Parallel programming for CPUs and GPUs with OpenMP and Python

GPPerf: Energy-Aware GPU Performance Prediction and Optimization Framework

Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach

HiRep

Scaling SU(2) to 1000 GPUs using HiRep

Gaia AVU-GSR

Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case study

gpuPairHMM: Ultra-fast GPU-based PairHMM for DNA Variant Calling

gpuPairHMM: High-speed Pair-HMM Forward Algorithm for DNA Variant Calling on GPUs

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high performance computing on graphics processing units: hgpu.org

Optimization of Data Assignment for Parallel Processing in a Hybrid Heterogeneous Environment Using Integer Linear Programming

Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures

Transparent FPGA Acceleration with TensorFlow

Dependable Embedded Systems

triSYCL for Xilinx FPGA

Computational Performance Predictions for Deep Neural Network Training: A Runtime-Based Approach

AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning

Why is FPGA-GPU Heterogeneity the Best Option for Embedded Deep Neural Networks?

C-for-Metal: High Performance SIMD Programming on Intel GPUs

Efficient MPI-based Communication for GPU-Accelerated Dask Applications

Text2Gestures: A Transformer-Based Network for Generating Emotive Body Gestures for Virtual Agents

Recent source codes

tdg-benchs: benchmarks used to test the performance of taskgraph

SW#SYCL

LLOR: Automatic Repair of OpenMP Programs

Solomon: Simple Off-LOading Macros Orchestrating multiple Notations

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

GPPerf: Energy-Aware GPU Performance Prediction and Optimization Framework

HiRep

Gaia AVU-GSR

gpuPairHMM: Ultra-fast GPU-based PairHMM for DNA Variant Calling

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