Parallel Iterative Linear Solvers on GPU: A Financial Engineering Case

Gaikwad, Abhijeet; Toke, Ioane Muni

doi:10.1109/pdp.2010.55

Cited by 21 publications

(10 citation statements)

References 27 publications

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“…To address this, different thread granularity options were investigated. By launching a large number of threads and a large number of thread blocks, the GPU runs the program in parallel on many threads (128) and many blocks (32).…”

Section: A Implementation Of the Monte Carlo Simulations On The Gpumentioning

confidence: 99%

“…In addition, they represent an investment at low cost when compared to multiple CPU cores. In terms of applications, many areas have already shown significant advantages of using GPUs illustrated with important computing performance increases, for example multitasking 28 , medical applications 29,30 or finance 31,32 . For air traffic management applications, Tandale et al provided one of the first GPU study contribution and accelerated by 30 times a CPU implementation of a large-scale Traffic Flow Management (TFM) problem with 17, 000 aircraft 33 .…”

Section: Introductionmentioning

confidence: 99%

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GPU-based Parallelization for Schedule Optimization with Uncertainty

Bosson

Xue

Zelinski

2014

14th AIAA Aviation Technology, Integration, and Operations Conference

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This paper presents an application of Graphics Processing Units (GPU) technology for speeding up a schedule optimization problem under uncertainty and provides a fast decision support algorithm to solve an air traffic management problem. In terminal airspace, integrated departure and arrival operations using shared resources have the potential to increase operations efficiency. However, results and benefits from integrated operations might be sensitive to flight time uncertainty. In previous work, a scheduling algorithm was proposed for a model of the Los Angeles terminal airspace. Uncertainty was introduced in the flight times and the uncertainty cost computation was handled by Monte Carlo simulations. The original implementation was carried out on sequential processors, but a 30-minute scenario ran in 6.5 hours, which prohibits applying the algorithm in real-time. This paper presents a GPU-based implementation of the scheduling optimization with uncertainty achieving a 637x speedup in Monte Carlo simulations and a 154x speedup for the entire algorithm compared to a sequential implementation. The runtime of the GPU-based code for the same 30-minute scenario is about 2.5 minutes. This significant speedup allows a large range of experiments to be explored and hundreds of simulations to be run. Two types of experiments are designed and they explore different values of traffic densities and arrival-to-departure ratios. The results demonstrate that there exist trade-off solutions between computed delays and number of controller interventions. The variation of total number of aircraft showed a larger impact on the controller's workload than the variation of arrival-to-departure ratios. When the traffic density is increased, compromise solutions can be identified to reduce the number of controller interventions and achieve low delays.

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Section: A Implementation Of the Monte Carlo Simulations On The Gpumentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GPU-based Parallelization for Schedule Optimization with Uncertainty

Bosson

Xue

Zelinski

2014

14th AIAA Aviation Technology, Integration, and Operations Conference

View full text Add to dashboard Cite

show abstract

“…In [14] iterative solvers that can take advantage of massive parallelism of general purpose GPUs for Stabilize BiConjugate Gradient (BiCGStab) and Conjugate Gradient Squared (CGS) methods of sparse linear systems with asymmetric coefficient matrices has been studied.…”

Section: Related Workmentioning

confidence: 99%

A high performance computing for AOM stock trading order matching using GPU

Rungraung

Uthayopas

2013

2013 International Computer Science and Engineering Conference (ICSEC)

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The task of trading orders matching in financial markets is a very challenging task since due to the speed of arriving request. In this paper, the GPUs technology and CUDA programming is explored as a potential technology to accelerate this task. The trading method in Automatic Order Matching (AOM) of Stock Exchange of Thailand (SET) is used as a case study. The code is developed and parallelized using GPU technology to speeding up the. The experimental results indicated that GPU can increase the performance for this process. In addition, the speedup of the work increases with the data size. Thus, the use of GPU can be substantially important when there is a need to handle massive data at a very high speed.

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“…The GPU (Graphics Processing Unit) as a highly parallel architecture allows the execution of such complex programs much faster than a CPU (Central Processing Unit) [13] [17]. Moreover, the development of new graphics parallel programming platforms such as CUDA [12] and OpenCL [29], has further urged programmers to move to GPUs, in order to take advantage of their high computing capacity [34].…”

Section: Introductionmentioning

confidence: 99%

Power Consumption Analysis of Parallel Algorithms on GPUs

Magoulès¹,

Ahamed²,

Desmaison³

et al. 2014

2014 IEEE Intl Conf on High Performance Computing and Communications, 2014 IEEE 6th Intl Symp on Cyberspace Safety and Security

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Due to their highly parallel multi-cores architecture, GPUs are being increasingly used in a wide range of computationally intensive applications. Compared to CPUs, GPUs can achieve higher performances at accelerating the programs' execution in an energy-efficient way. Therefore GPGPU computing is useful for high performance computing applications and in many scientific research fields. In order to bring further performance improvements, GPU clusters are increasingly adopted. The energy consumed by GPUs cannot be neglected. Therefore, an energyefficient time scheduling of the programs that are going to be executed by the parallel GPUs based on their deadline as well as the assigned priorities could be deployed to face their energetic avidity. For this reason, we present in this paper a model enabling the measure of the power consumption and the time execution of some elementary operations running on a single GPU using a new developed energy measurement protocol. Consequently, using our methodology, energy needs of a program could be predicted, allowing a better task scheduling.

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Parallel Iterative Linear Solvers on GPU: A Financial Engineering Case

Cited by 21 publications

References 27 publications

GPU-based Parallelization for Schedule Optimization with Uncertainty

GPU-based Parallelization for Schedule Optimization with Uncertainty

A high performance computing for AOM stock trading order matching using GPU

Power Consumption Analysis of Parallel Algorithms on GPUs

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