high performance computing on graphics processing units: hgpu.org

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

Tutoring LLM into a Better CUDA Optimizer

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

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high performance computing on graphics processing units: hgpu.org

Programming

Energy efficiency of finite difference algorithms on multicore CPUs, GPUs, and Intel Xeon Phi processors

FPGA implementation of a Convolutional Neural Network for "Wake up word" detection

Computing Treewidth on the GPU

An Efficient Load Balancing Method for Tree Algorithms

Performance Evaluation of Container-based Virtualization for High Performance Computing Environments

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

GALARIO: a GPU Accelerated Library for Analysing Radio Interferometer Observations

Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices

Accelerating Radio Astronomy with Auto-Tuning

Asynchronous Task-Based Polar Decomposition on Single Node Manycore Architectures

Distributed Training Large-Scale Deep Architectures

Recent source codes

OpScanner

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

transformers_tvm: Implementation of Encoder Decoder transformer on TVM

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

AutoDock-GPU: AutoDock for GPUs and other accelerators

NCCLX: collective communication framework

Tutoring LLM into a Better CUDA Optimizer

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

Kernel Library for LLM Serving

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

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