Using Compiler Directives for Performance Portability in Scientific Computing: Kernels from Molecular Simulation

Sedova, Ada; Tillack, Andreas F.; Tharrington, Arnold

doi:10.1007/978-3-030-12274-4_2

Cited by 4 publications

(2 citation statements)

References 48 publications

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“…Numerous studies have focused on directive-based offloading as a solution for performance portability over the past decade [3], [5], [9], [10], [12], [13], [21]. Multiple reports compared the OpenACC and OpenMP approaches and explored difference in usage and performance on GPUs compared to CUDA, and for CPU-based threading and accelerators like the Xeon Phi [2], [5], [10], [12], using simplified kernels and miniapps.…”

Section: Related Workmentioning

confidence: 99%

Portability for GPU-accelerated molecular docking applications for cloud and HPC: can portable compiler directives provide performance across all platforms?

Thavappiragasam¹,

Elwasif²,

Sedova³

2022

Preprint

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High-throughput structure-based screening of druglike molecules has become a common tool in biomedical research. Recently, acceleration with graphics processing units (GPUs) has provided a large performance boost for molecular docking programs. Both cloud and high-performance computing (HPC) resources have been used for large screens with molecular docking programs; while NVIDIA GPUs have dominated cloud and HPC resources, new vendors such as AMD and Intel are now entering the field, creating the problem of software portability across different GPUs. Ideally, software productivity could be maximized with portable programming models that are able to maintain high performance across architectures. While in many cases compiler directives have been used as an easy way to offload parallel regions of a CPU-based program to a GPU accelerator, they may also be an attractive programming model for providing portability across different GPU vendors, in which case the porting process may proceed in the reverse direction: from low-level, architecture-specific code to higherlevel directive-based abstractions. MiniMDock is a new miniapplication (miniapp) designed to capture the essential computational kernels found in molecular docking calculations, such as are used in pharmaceutical drug discovery efforts, in order to test different solutions for porting across GPU architectures. Here we extend MiniMDock to GPU offloading with OpenMP directives, and compare to performance of kernels using CUDA, and HIP on both NVIDIA and AMD GPUs, as well as across different compilers, exploring performance bottlenecks. We document this reverse-porting process, from highly optimized device code to a higher-level version using directives, compare code structure, and describe barriers that were overcome in this effort.

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Section: Related Workmentioning

confidence: 99%

Portability for GPU-accelerated molecular docking applications for cloud and HPC: can portable compiler directives provide performance across all platforms?

Thavappiragasam¹,

Elwasif²,

Sedova³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The miniapp was designed based on two existing versions of the full AutoDock-GPU program: the new CUDA version developed with the help of NVIDIA, 2 as well as a Kokkos version. 3 Both of these were created in an effort to provide a version of the original GPU-accelerated AutoDock-GPU program, developed by Scripps Research in OpenCL, 4 that could be deployed on Summit for drug discovery efforts against COVID-19. There are currently no NVIDIA drivers for OpenCL on Power9, which prevents the original version of the program from running on Summit.…”

Section: B Design and Structurementioning

confidence: 99%

Performance Portability of Molecular Docking Miniapp On Leadership Computing Platforms

Thavappiragasam

Scheinberg²,

Elwasif

et al. 2020

2020 IEEE/ACM International Workshop on Performance, Portability and Productivity in HPC (P3HPC)

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Rapidly changing computer architectures, such as those found at high-performance computing (HPC) facilities, present the need for mini-applications (miniapps) that capture essential algorithms used in large applications to test program performance and portability, aiding transitions to new systems. The COVID-19 pandemic has fueled a flurry of activity in computational drug discovery, including the use of supercomputers and GPU acceleration for massive virtual screens for therapeutics. Recent work targeting COVID-19 at the Oak Ridge Leadership Computing Facility (OLCF) used the GPU-accelerated program AutoDock-GPU to screen billions of compounds on the Summit supercomputer. In this paper we present the development of a new miniapp, miniAutoDock-GPU, that can be used to evaluate the performance and portability of GPU-accelerated proteinligand docking programs on different computer architectures. These tests are especially relevant as facilities transition from petascale systems and prepare for upcoming exascale systems that will use a variety of GPU vendors. The key calculations, namely, the Lamarckian genetic algorithm combined with a local search using a Solis-Wets based random optimization algorithm, are implemented. We developed versions of the miniapp using several different programming models for GPU acceleration, including a version using the CUDA runtime API for NVIDIA GPUs, and the Kokkos middle-ware API which is facilitated by C++ template libraries. A third version, currently in progress, uses the HIP programming model. These efforts will help facilitate the transition to exascale systems for this important emerging HPC application, as well as its use on a wide range of heterogeneous platforms.Index Terms-heterogeneous system, high-performance computing, performance portability, hybrid parallel programming model, molecular docking, drug discovery.

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Porting Adaptive Ensemble Molecular Dynamics Workflows to the Summit Supercomputer

Ossyra

Sedova

Tharrington

et al. 2019

Lecture Notes in Computer Science

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Using Compiler Directives for Performance Portability in Scientific Computing: Kernels from Molecular Simulation

Cited by 4 publications

References 48 publications

Portability for GPU-accelerated molecular docking applications for cloud and HPC: can portable compiler directives provide performance across all platforms?

Portability for GPU-accelerated molecular docking applications for cloud and HPC: can portable compiler directives provide performance across all platforms?

Performance Portability of Molecular Docking Miniapp On Leadership Computing Platforms

Porting Adaptive Ensemble Molecular Dynamics Workflows to the Summit Supercomputer

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