Process Simulation of Complex Biochemical Pathways in Explicit 3D Space Enabled by Heterogeneous Computing Platform

2014 ◽  
Author(s):  
Jie Li ◽  
Amin Salighehdar ◽  
Narayan Ganesan
Keyword(s):  
Process Simulation ◽  
Heterogeneous Computing ◽  
Computing Platform ◽  
Biochemical Pathways ◽  
3D Space ◽  
Heterogeneous Computing Platform

scholarly journals Accelerate Scientific Deep Learning Models on Heterogeneous Computing Platform with FPGA

2020 ◽  
Vol 245 ◽  
pp. 09014
Author(s):  
Chao Jiang ◽  
David Ojika ◽  
Sofia Vallecorsa ◽  
Thorsten Kurth ◽  
Prabhat ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Analysis ◽  
Heterogeneous Computing ◽  
Scientific Data ◽  
Great Promise ◽  
University Of Florida ◽  
Computing Platform ◽  
Scientific Data Analysis ◽  
The University ◽  
Heterogeneous Computing Platform

AI and deep learning are experiencing explosive growth in almost every domain involving analysis of big data. Deep learning using Deep Neural Networks (DNNs) has shown great promise for such scientific data analysis applications. However, traditional CPU-based sequential computing without special instructions can no longer meet the requirements of mission-critical applications, which are compute-intensive and require low latency and high throughput. Heterogeneous computing (HGC), with CPUs integrated with GPUs, FPGAs, and other science-targeted accelerators, offers unique capabilities to accelerate DNNs. Collaborating researchers at SHREC1at the University of Florida, CERN Openlab, NERSC2at Lawrence Berkeley National Lab, Dell EMC, and Intel are studying the application of heterogeneous computing (HGC) to scientific problems using DNN models. This paper focuses on the use of FPGAs to accelerate the inferencing stage of the HGC workflow. We present case studies and results in inferencing state-of-the-art DNN models for scientific data analysis, using Intel distribution of OpenVINO, running on an Intel Programmable Acceleration Card (PAC) equipped with an Arria 10 GX FPGA. Using the Intel Deep Learning Acceleration (DLA) development suite to optimize existing FPGA primitives and develop new ones, we were able accelerate the scientific DNN models under study with a speedup from 2.46x to 9.59x for a single Arria 10 FPGA against a single core (single thread) of a server-class Skylake CPU.

scholarly journals Nonlinear predictive control on a heterogeneous computing platform

2018 ◽  
Vol 78 ◽  
pp. 105-115 ◽  
Author(s):  
Bulat Khusainov ◽  
Eric Kerrigan ◽  
Andrea Suardi ◽  
George Constantinides
Keyword(s):  
Predictive Control ◽  
Heterogeneous Computing ◽  
Computing Platform ◽  
Nonlinear Predictive Control ◽  
Heterogeneous Computing Platform

scholarly journals Nonlinear predictive control on a heterogeneous computing platform

2017 ◽  
Vol 50 (1) ◽  
pp. 11877-11882 ◽  
Author(s):  
Bulat Khusainov ◽  
Eric C. Kerrigan ◽  
Andrea Suardi ◽  
George A. Constantinides
Keyword(s):  
Predictive Control ◽  
Heterogeneous Computing ◽  
Computing Platform ◽  
Nonlinear Predictive Control ◽  
Heterogeneous Computing Platform
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