AMIDE v2: High-Throughput Screening Based on AutoDock-GPU and Improved Workflow Leading to Better Performance and Reliability

Molecular docking is widely used in computed drug discovery and biological target identification, but getting fast results can be tedious and often requires supercomputing solutions. AMIDE stands for AutoMated Inverse Docking Engine. It was initially developed in 2014 to perform inverse docking on High Performance Computing. AMIDE version 2 brings substantial speed-up improvement by using AutoDock-GPU and by pulling a total revision of programming workflow, leading to better performances, easier use, bug corrections, parallelization improvements and PC/HPC compatibility. In addition to inverse docking, AMIDE is now an optimized tool capable of high throughput inverse screening. For instance, AMIDE version 2 allows acceleration of the docking up to 12.4 times for 100 runs of AutoDock compared to version 1, without significant changes in docking poses. The reverse docking of a ligand on 87 proteins takes only 23 min on 1 GPU (Graphics Processing Unit), while version 1 required 300 cores to reach the same execution time. Moreover, we have shown an exponential acceleration of the computation time as a function of the number of GPUs used, allowing a significant reduction of the duration of the inverse docking process on large datasets.

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Analysis of GPU Computation of Parabolic, Bessel, Wright and Riemann Zeta Functions

ITM Web of Conferences ◽

10.1051/itmconf/20214002005 ◽

2021 ◽

Vol 40 ◽

pp. 02005

Author(s):

Ashish A. Jadhav ◽

Abhijeet D. Kalamkar ◽

Pritish A. Gaikwad ◽

Vishwesh Vyawahare ◽

Navin Singhaniya

Keyword(s):

Fractional Calculus ◽

Gpu Computing ◽

Graphics Processing Unit ◽

Zeta Functions ◽

Computation Time ◽

Processing Unit ◽

Mathematical Functions ◽

Speed Up ◽

Sequential Code ◽

Graphics Processing

This paper deals with GPU computing of special mathematical functions that are used in Fractional Calculus. The graphics processing unit (GPU) has grown to be an integral part of nowadays’s mainstream computing structures. The special mathematical functions are an integral part of Fractional Calculus. This paper deals with a novel parallel approach for computing special mathematical functions used in Fractional Calculus. NVIDIA’s GPU hardware is used to speed up the parallel algorithm. A comparison of the sequential code, vectorized code and GPU code is performed. We have successfully reduced the computation time of special mathematical functions using the parallel computing capabilities of GPU.

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A lightweight approach to performance portability with targetDP

The International Journal of High Performance Computing Applications ◽

10.1177/1094342016682071 ◽

2016 ◽

Vol 32 (2) ◽

pp. 288-301

Author(s):

Alan Gray ◽

Kevin Stratford

Keyword(s):

Particle Physics ◽

Message Passing ◽

Graphics Processing Units ◽

High Performance ◽

Large Scale ◽

Message Passing Interface ◽

Graphics Processing Unit ◽

Processing Unit ◽

Performance Portability ◽

Graphics Processing

Leading high performance computing systems achieve their status through use of highly parallel devices such as NVIDIA graphics processing units or Intel Xeon Phi many-core CPUs. The concept of performance portability across such architectures, as well as traditional CPUs, is vital for the application programmer. In this paper we describe targetDP, a lightweight abstraction layer which allows grid-based applications to target data parallel hardware in a platform agnostic manner. We demonstrate the effectiveness of our pragmatic approach by presenting performance results for a complex fluid application (with which the model was co-designed), plus separate lattice quantum chromodynamics particle physics code. For each application, a single source code base is seen to achieve portable performance, as assessed within the context of the Roofline model. TargetDP can be combined with Message Passing Interface (MPI) to allow use on systems containing multiple nodes: we demonstrate this through provision of scaling results on traditional and graphics processing unit-accelerated large scale supercomputers.

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Embedded GPU Implementation for High-Performance Ultrasound Imaging

Electronics ◽

10.3390/electronics10080884 ◽

2021 ◽

Vol 10 (8) ◽

pp. 884

Author(s):

Stefano Rossi ◽

Enrico Boni

Keyword(s):

High Performance ◽

Graphics Processing Unit ◽

Digital Signal ◽

Processing Unit ◽

Embedded Computing ◽

Field Programmable ◽

Peripheral Component Interconnect ◽

Programmable Gate Arrays ◽

Graphics Processing ◽

Signal Processors

Methods of increasing complexity are currently being proposed for ultrasound (US) echographic signal processing. Graphics Processing Unit (GPU) resources allowing massive exploitation of parallel computing are ideal candidates for these tasks. Many high-performance US instruments, including open scanners like ULA-OP 256, have an architecture based only on Field-Programmable Gate Arrays (FPGAs) and/or Digital Signal Processors (DSPs). This paper proposes the implementation of the embedded NVIDIA Jetson Xavier AGX module on board ULA-OP 256. The system architecture was revised to allow the introduction of a new Peripheral Component Interconnect Express (PCIe) communication channel, while maintaining backward compatibility with all other embedded computing resources already on board. Moreover, the Input/Output (I/O) peripherals of the module make the ultrasound system independent, freeing the user from the need to use an external controlling PC.

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Finite element method completely implemented for graphic processor units using parallel algorithm libraries

The International Journal of High Performance Computing Applications ◽

10.1177/1094342017694703 ◽

2017 ◽

Vol 33 (1) ◽

pp. 53-66 ◽

Cited By ~ 1

Author(s):

Franz Pichler ◽

Gundolf Haase

Keyword(s):

Finite Element ◽

Graphics Processing Unit ◽

Computational Cost ◽

Processing Unit ◽

Time Step ◽

Device Architecture ◽

Transient Problems ◽

Speed Up ◽

Automotive Batteries ◽

Graphics Processing

A finite element code is developed in which all of the computationally expensive steps are performed on a graphics processing unit via the THRUST and the PARALUTION libraries. The code focuses on the simulation of transient problems where the repeated computations per time-step create the computational cost. It is used to solve partial and ordinary differential equations as they arise in thermal-runaway simulations of automotive batteries. The speed-up obtained by utilizing the graphics processing unit for every critical step is compared against the single core and the multi-threading solutions which are also supported by the chosen libraries. This way a high total speed-up on the graphics processing unit is achieved without the need for programming a single classical Compute Unified Device Architecture kernel.

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Implementation of a Semi-Implicit Pressure-Based Multigrid Fluid Flow Algorithm on a Graphics Processing Unit

Volume 13: New Developments in Simulation Methods and Software for Engineering Applications; Safety Engineering, Risk Analysis and Reliability Methods; Transportation Systems ◽

10.1115/imece2009-11587 ◽

2009 ◽

Cited By ~ 5

Author(s):

Aaron F. Shinn ◽

S. P. Vanka

Keyword(s):

Stokes Equations ◽

Graphics Processing Unit ◽

Navier Stokes ◽

Processing Unit ◽

Navier Stokes Equations ◽

Driven Cavity ◽

Multigrid Algorithm ◽

Computational Speed ◽

Speed Up ◽

Graphics Processing

A semi-implicit pressure based multigrid algorithm for solving the incompressible Navier-Stokes equations was implemented on a Graphics Processing Unit (GPU) using CUDA (Compute Unified Device Architecture). The multigrid method employed was the Full Approximation Scheme (FAS), which is used for solving nonlinear equations. This algorithm is applied to the 2D driven cavity problem and compared to the CPU version of the code (written in Fortran) to assess computational speed-up.

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High-Performance, Graphics Processing Unit-Accelerated Fock Build Algorithm

Journal of Chemical Theory and Computation ◽

10.1021/acs.jctc.0c00768 ◽

2020 ◽

Vol 16 (12) ◽

pp. 7232-7238

Author(s):

Giuseppe M. J. Barca ◽

Jorge L. Galvez-Vallejo ◽

David L. Poole ◽

Alistair P. Rendell ◽

Mark S. Gordon

Keyword(s):

High Performance ◽

Graphics Processing Unit ◽

Processing Unit ◽

Graphics Processing

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Ballooning Graphics Memory Space in Full GPU Virtualization Environments

Scientific Programming ◽

10.1155/2019/5240956 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11

Author(s):

Younghun Park ◽

Minwoo Gu ◽

Sungyong Park

Keyword(s):

High Performance ◽

Virtual Machines ◽

Graphics Processing Unit ◽

Performance Degradation ◽

Processing Unit ◽

Memory Space ◽

Memory Size ◽

Memory Sharing ◽

Gpu Virtualization ◽

Graphics Processing

Advances in virtualization technology have enabled multiple virtual machines (VMs) to share resources in a physical machine (PM). With the widespread use of graphics-intensive applications, such as two-dimensional (2D) or 3D rendering, many graphics processing unit (GPU) virtualization solutions have been proposed to provide high-performance GPU services in a virtualized environment. Although elasticity is one of the major benefits in this environment, the allocation of GPU memory is still static in the sense that after the GPU memory is allocated to a VM, it is not possible to change the memory size at runtime. This causes underutilization of GPU memory or performance degradation of a GPU application due to the lack of GPU memory when an application requires a large amount of GPU memory. In this paper, we propose a GPU memory ballooning solution called gBalloon that dynamically adjusts the GPU memory size at runtime according to the GPU memory requirement of each VM and the GPU memory sharing overhead. The gBalloon extends the GPU memory size of a VM by detecting performance degradation due to the lack of GPU memory. The gBalloon also reduces the GPU memory size when the overcommitted or underutilized GPU memory of a VM creates additional overhead for the GPU context switch or the CPU load due to GPU memory sharing among the VMs. We implemented the gBalloon by modifying the gVirt, a full GPU virtualization solution for Intel’s integrated GPUs. Benchmarking results show that the gBalloon dynamically adjusts the GPU memory size at runtime, which improves the performance by up to 8% against the gVirt with 384 MB of high global graphics memory and 32% against the gVirt with 1024 MB of high global graphics memory.

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