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FPGA-BASED HYBRID COMPUTING FOR ESS LINAC SIMULATOR.

Jeevaraj, Arun LU (2017) EITM02 20171
Department of Electrical and Information Technology
Abstract
The thesis explores efficient implementation strategies for the European Spallation Source (ESS) linear accelerator simulator. The target simulator needs to run at real time, requires high computation accuracy, and should be scalable for high density beam scenarios. The high data processing, communication, and storage requirements due to a large data set, along with a strict accuracy requirement, poses a critical implementation challenge for traditional computing platforms.

To tackle these issues, this thesis uses a scalable platform with hybrid computing capabilities and the OpenCL framework for an unified programming model. The hybrid computing platform allows for mapping tasks to the most suitable hardware and explores heterogeneous... (More)
The thesis explores efficient implementation strategies for the European Spallation Source (ESS) linear accelerator simulator. The target simulator needs to run at real time, requires high computation accuracy, and should be scalable for high density beam scenarios. The high data processing, communication, and storage requirements due to a large data set, along with a strict accuracy requirement, poses a critical implementation challenge for traditional computing platforms.

To tackle these issues, this thesis uses a scalable platform with hybrid computing capabilities and the OpenCL framework for an unified programming model. The hybrid computing platform allows for mapping tasks to the most suitable hardware and explores heterogeneous memory hierarchy to fast data shuffling. The OpenCL framework allows functional portability and scalability across different target devices such as CPU, GPU and OpenCL accelerator devices like with FPGA and DSP arrays. The computational intensive tasks of the simulator can be conveniently mapped to the accelerators, where computational parallelism is explored.

The targeted simulator is implemented in a Xilinx hybrid computing platform, consisting of an Intel i7 CPU, an Nvidia 960 GPU, and a Xilinx Kintex Ultra-scale FPGA. Comparing to the benchmark (a C++ based implementation), we are able to accelerate the ESS simulator by more than 80x on the GPU and 25x with FPGA, with the same simulation accuracy (double precision floating point). We identified the implementation bottleneck on the specific platform, which is the memory bandwidth. This leads to our future work. One important future task is to investigate different hybrid computing platforms of different vendors, considering computation capability, memory bandwidth, as well as design software. Moreover, different data types will be examined, including fixed-point, double/single-precision floating point, or custom floating point. (Less)
Popular Abstract
The European Spallation Source is one of Europe's largest research infrastructures to bring new insights into the challenges of science and innovation in fields as diverse as material and life sciences, energy, environmental technology, cultural heritage,solid-state and fundamental physics by the end of the decade. A 5 MW, long pulse proton accelerator is used to reach this goal. The particles in this beam contains so much of energy, that the loses to the super-conducting structures become marginally critical. An accurate and real-time simulation model to predict the behaviour of the particles is required to minimize the losses. There are two approaches to model the particle behaviour, one is to use an approximation with the envelop of the... (More)
The European Spallation Source is one of Europe's largest research infrastructures to bring new insights into the challenges of science and innovation in fields as diverse as material and life sciences, energy, environmental technology, cultural heritage,solid-state and fundamental physics by the end of the decade. A 5 MW, long pulse proton accelerator is used to reach this goal. The particles in this beam contains so much of energy, that the loses to the super-conducting structures become marginally critical. An accurate and real-time simulation model to predict the behaviour of the particles is required to minimize the losses. There are two approaches to model the particle behaviour, one is to use an approximation with the envelop of the beam and the other is to model each particle and track them. The envelop method can run real-time, but has discrepancies to the actual behaviour of the beam at high energy configurations, hence a computationally intensive mult-particle simulation model is required to provide realistic behaviour. This is achieved by supporting non-linear behaviour such as space charge (particle to particle interactions). The multi-particle simulation algorithm has linear and non-linear attributes, where the linear section can map efficiently to the graphic processor hardware architecture, while the non-linear section can map more efficiently to the flexible FPGA fabrics. To make the multi-particle simulation model run real-time, a computing platform that can support graphic processors and FPGA accelerators are required. The thesis proposes to use a hybrid computing platform with FPGA, GPU and CPU to provide the efficient mapping of the multi-particle simulation model. A unified development environment and a scalable platform is developed by using a software stack build based on OpenCL framework. This allowed to develop the system in a rapid design phase and maximize the code re-use. We were able to accelerate the linear part of the simulation model by upto 89x on the given set of hardware. Different ways to further optimize the solution are also explored. (Less)
Please use this url to cite or link to this publication:
author
Jeevaraj, Arun LU
supervisor
organization
alternative title
FPGA-baserad hybridberäkningsenhet för simulering av ESS linjäraccelerator.
course
EITM02 20171
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Hybrid Computing, OpenCL, FPGA, Linear Accelerator, Heterogeneous computing
report number
LU/LTH-EIT 2017-609
language
English
id
8928453
date added to LUP
2017-11-13 11:57:00
date last changed
2017-11-13 11:57:00
@misc{8928453,
  abstract     = {The thesis explores efficient implementation strategies for the European Spallation Source (ESS) linear accelerator simulator. The target simulator needs to run at real time, requires high computation accuracy, and should be scalable for high density beam scenarios. The high data processing, communication, and storage requirements due to a large data set, along with a strict accuracy requirement, poses a critical implementation challenge for traditional computing platforms. 

To tackle these issues, this thesis uses a scalable platform with hybrid computing capabilities and the OpenCL framework for an unified programming model. The hybrid computing platform allows for mapping tasks to the most suitable hardware and explores heterogeneous memory hierarchy to fast data shuffling. The OpenCL framework allows functional portability and scalability across different target devices such as CPU, GPU and OpenCL accelerator devices like with FPGA and DSP arrays. The computational intensive tasks of the simulator can be conveniently mapped to the accelerators, where computational parallelism is explored. 

The targeted simulator is implemented in a Xilinx hybrid computing platform, consisting of an Intel i7 CPU, an Nvidia 960 GPU, and a Xilinx Kintex Ultra-scale FPGA. Comparing to the benchmark (a C++ based implementation), we are able to accelerate the ESS simulator by more than 80x on the GPU and 25x with FPGA, with the same simulation accuracy (double precision floating point). We identified the implementation bottleneck on the specific platform, which is the memory bandwidth. This leads to our future work. One important future task is to investigate different hybrid computing platforms of different vendors, considering computation capability, memory bandwidth, as well as design software. Moreover, different data types will be examined, including fixed-point, double/single-precision floating point, or custom floating point.},
  author       = {Jeevaraj, Arun},
  keyword      = {Hybrid Computing,OpenCL,FPGA,Linear Accelerator,Heterogeneous computing},
  language     = {eng},
  note         = {Student Paper},
  title        = {FPGA-BASED HYBRID COMPUTING FOR ESS LINAC SIMULATOR.},
  year         = {2017},
}