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Predicting the Execution Time of a kernel on a specific GPU using PTX code

Monika Dagar, Jorge Roldan

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX 1650, nVidia GeForce GTX Titan XP, Performance, PTX, Tesla K20, Tesla K80, Tesla M60, Tesla P100, Tesla T4, Tesla V100

October 22, 2023 by hgpu

Novel Parallelization Strategies for High-Performance DNN Training on HPC Systems

Arpan Jain

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Tags: Artificial intelligence, Computer science, CUDA, Data parallelism, Deep learning, HPC, Machine learning, MPI, Neural networks, nVidia, Speech recognition, Tesla K80, Tesla V100, Thesis

October 1, 2023 by hgpu

TLP: A Deep Learning-based Cost Model for Tensor Program Tuning

Yi Zhai, Yu Zhang, Shuo Liu, Xiaomeng Chu, Jie Peng, Jianmin Ji, Yanyong Zhang

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Tags: Computer science, CUDA, Deep learning, nVidia, Optimization, Package, Tesla K80, Tesla T4

February 12, 2023 by hgpu

A Symbolic Emulator for Shuffle Synthesis on the NVIDIA PTX Code

Kazuaki Matsumura, Simon Garcia De Gonzalo, Antonio J. Peña

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Tags: Benchmarking, Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, OpenACC, Package, PTX, Tesla K40, Tesla K80, Tesla V100

February 5, 2023 by hgpu

OpenMP Advisor

Alok Mishra, Abid M. Malik, Meifeng Lin, Barbara Chapman

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Tags: AMD Radeon Instinct Mi50, ATI, Benchmarking, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce RTX 2080, OpenMP, Tesla A100, Tesla K80, Tesla V100

January 15, 2023 by hgpu

BaCO: A Fast and Portable Bayesian Compiler Optimization Framework

Erik Hellsten, Artur Souza, Johannes Lenfers, Rubens Lacouture, Olivia Hsu, Adel Ejjeh, Fredrik Kjolstad, Michel Steuwer, Kunle Olukotun, Luigi Nardi

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Tags: Bayesian, Compilers, Computer science, FPGA, nVidia, OpenCL, Package, Performance, Programming Languages, Tesla K80

January 8, 2023 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

Performance prediction of deep learning applications training in GPU as a service systems

Marco Lattuada, Eugenio Gianniti, Danilo Ardagna, Li Zhang

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Tags: Computer science, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, Performance, Tesla K80, Tesla M60

January 30, 2022 by hgpu

On the accuracy and performance of the lattice Boltzmann method with 64-bit, 32-bit and novel 16-bit number formats

Moritz Lehmann, Mathias J. Krause, Giorgio Amati, Marcello Sega, Jens Harting, Stephan Gekle

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Tags: AMD Radeon Instinct MI100, AMD Radeon VI, ATI, Fluid dynamics, lattice Boltzmann, Mixed precision, nVidia, OpenCL, Tesla K20, Tesla K40, Tesla K80, Tesla P100, Tesla V100

December 19, 2021 by hgpu

Benchmarking the Nvidia GPU Lineage: From Early K80 to Modern A100 with Asynchronous Memory Transfers

Martin Svedin, Steven W. D. Chien, Gibson Chikafa, Niclas Jansson, Artur Podobas

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Tags: Benchmarking, Computer science, MPI, nVidia, nVidia GeForce GTX 1050 Ti, nVidia Quadro K 2200, Tesla A100, Tesla K80, Tesla P100, Tesla V100

June 20, 2021 by hgpu

A Tensor Compiler for Unified Machine Learning Prediction Serving

Supun Nakandala Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, Matteo Interlandi

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Tags: Computer science, CUDA, Machine learning, nVidia, Package, Tesla K80, Tesla P100

October 18, 2020 by hgpu

Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads

Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, Amar Phanishayee, Matei Zaharia

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Tags: Computer science, CUDA, Deep learning, FPGA, GPU cluster, Heterogeneous systems, nVidia, Optimization, Task scheduling, Tesla K80, Tesla P100, Tesla V100

August 30, 2020 by hgpu

Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C+

CHARM-SYCL: New Unified Programming Environment for Multiple Accelerator Types

AFOCL: Portable OpenCL Programming of FPGAs via Automated Built-in Kernel Management

pp-template: Template for Performance-Portable code development, targeting HOST-DEVICE computer architectures

An approach to performance portability through generic programming

Redco: A Lightweight Tool to Automate Distributed Training

Redco: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs

OpenRAND: Reproducible Random Number For Parallel Computations

OpenRAND: A Performance Portable, Reproducible Random Number Generation Library for Parallel Computations

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high performance computing on graphics processing units: hgpu.org

Predicting the Execution Time of a kernel on a specific GPU using PTX code

Novel Parallelization Strategies for High-Performance DNN Training on HPC Systems

TLP: A Deep Learning-based Cost Model for Tensor Program Tuning

A Symbolic Emulator for Shuffle Synthesis on the NVIDIA PTX Code

OpenMP Advisor

BaCO: A Fast and Portable Bayesian Compiler Optimization Framework

End-to-end Optimization of Machine Learning Prediction Queries

Performance prediction of deep learning applications training in GPU as a service systems

On the accuracy and performance of the lattice Boltzmann method with 64-bit, 32-bit and novel 16-bit number formats

Benchmarking the Nvidia GPU Lineage: From Early K80 to Modern A100 with Asynchronous Memory Transfers

A Tensor Compiler for Unified Machine Learning Prediction Serving

Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads

Recent source codes

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Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C+

CHARM-SYCL: New Unified Programming Environment for Multiple Accelerator Types

AFOCL: Portable OpenCL Programming of FPGAs via Automated Built-in Kernel Management

pp-template: Template for Performance-Portable code development, targeting HOST-DEVICE computer architectures

Redco: A Lightweight Tool to Automate Distributed Training

OpenRAND: Reproducible Random Number For Parallel Computations

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