high performance computing on graphics processing units: hgpu.org

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Portable, Scalable Approaches for Improving Asynchronous Many-Task Runtime Node Use

John Keith Holmen

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Tags: Computer science, CUDA, Heterogeneous systems, MPI, nVidia, nVidia GeForce GTX Titan X, performance portability, Task scheduling

June 5, 2022 by hgpu

FELARE: Fair Scheduling of Machine Learning Applications on Heterogeneous Edge Systems

Ali Mokhtari, Pooyan Jamshidi, Mohsen Amini Salehi

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Tags: Computer science, FPGA, Heterogeneous systems, Machine learning, nVidia, Package, Tesla M60

June 5, 2022 by hgpu

Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning

Chengfei Lv, Chaoyue Niu, Renjie Gu, Xiaotang Jiang, Zhaode Wang, Bin Liu, Ziqi Wu, Qiulin Yao, Congyu Huang, Panos Huang, Tao Huang, Hui Shu, Jinde Song, Bin Zou, Peng Lan, Guohuan Xu, Fei Wu, Shaojie Tang, Fan Wu, Guihai Chen

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Tags: Cloud, Computer science, Machine learning, Neural networks, nVidia, nVidia GeForce RTX 2080 Ti, Python

June 5, 2022 by hgpu

End-to-end Optimization of Machine Learning Prediction Queries

Kwanghyun Park, Karla Saur, Dalitso Banda, Rathijit Sen, Matteo Interlandi, Konstantinos Karanasos

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Tags: Computer science, Databases, Machine learning, nVidia, Tesla K80, Tesla P100

June 5, 2022 by hgpu

Fast GPU bounding boxes on tree-structured scenes

Raph Levien

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Tags: 3D Graphics and Realism, AMD Radeon RX 5700 XT, ATI, GLSL, HLSL, nVidia, nVidia GeForce GTX 1080, Package, Rendering, Vulkan

May 29, 2022 by hgpu

User’s needs influencing HPC technologies

E. Athanasaki, N. Meyer, M. Cestari, A.Tuncer Durak, A. Tekin, P. Gschwandtner

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Tags: AI, Artificial intelligence, Cloud, Computer science, CUDA, HIP, HPC, nVidia, OpenCL, Security, SYCL

May 29, 2022 by hgpu

SOL: Reducing the Maintenance Overhead for Integrating Hardware Support into AI Frameworks

Nicolas Weber

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Tags: AI, Artificial intelligence, Computer science, CUDA, Machine learning, Neural networks, nVidia, nVidia GeForce RTX 2080, Performance

May 29, 2022 by hgpu

Fault Injection techniques for GPU Reliability Evaluation

Luigi Galasso

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Tags: Computer science, CUDA, Fault simulation, nVidia, Thesis

May 29, 2022 by hgpu

Lossless Acceleration for Seq2seq Generation with Aggressive Decoding

Tao Ge, Heming Xia, Xin Sun, Si-Qing Chen, Furu Wei

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Tags: Computer science, CUDA, Machine learning, NLP, nVidia, Package, Tesla V100

May 29, 2022 by hgpu

Blockchain Goes Green? Part II: Characterizing the Performance and Cost of Blockchains on the Cloud and at the Edge

Dumitrel Loghin, Tien Tuan Anh Dinh, Aung Maw, Chen Gang, Yong Meng Teo, Beng Chin Ooi

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Tags: Cloud, Computer science, Finance, nVidia, nVidia Jetson TX2, Package

May 22, 2022 by hgpu

GPU Ray Tracing with Monte Carlo Methods

Daniele Alma

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Tags: 3D Graphics and Realism, Algorithms, nVidia, nVidia GeForce GTX 930 MX, OpenCL, Raytracing, Rendering, Thesis

May 22, 2022 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

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high performance computing on graphics processing units: hgpu.org

Portable, Scalable Approaches for Improving Asynchronous Many-Task Runtime Node Use

FELARE: Fair Scheduling of Machine Learning Applications on Heterogeneous Edge Systems

End-to-end Optimization of Machine Learning Prediction Queries

Fast GPU bounding boxes on tree-structured scenes

SOL: Reducing the Maintenance Overhead for Integrating Hardware Support into AI Frameworks

Fault Injection techniques for GPU Reliability Evaluation

Lossless Acceleration for Seq2seq Generation with Aggressive Decoding

GPU Ray Tracing with Monte Carlo Methods

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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