The hardware accelerator debate: A financial risk case study using many-core computing

Varghese, Blesson

doi:10.1016/j.compeleceng.2015.01.012

Cited by 6 publications

(3 citation statements)

References 34 publications

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“…The rCUDA framework suits such an application because minimal changes need to be brought about to the production cluster and the acceleration required for the analysis is obtained as a service from a remote host. The analysis has previously been investigated in the context of many-core architectures [11], but we believe virtual GPUs can be a better option.…”

Section: A Financial Risk Case Studymentioning

confidence: 99%

Acceleration-as-a-Service: Exploiting Virtualised GPUs for a Financial Application

Varghese

Prades

Reaño

et al. 2015

2015 IEEE 11th International Conference on E-Science

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How can GPU acceleration be obtained as a service in a cluster? This question has become increasingly significant due to the inefficiency of installing GPUs on all nodes of a cluster. The research reported in this paper is motivated to address the above question by employing rCUDA (remote CUDA), a framework that facilitates Acceleration-as-a-Service (AaaS), such that the nodes of a cluster can request the acceleration of a set of remote GPUs on demand. The rCUDA framework exploits virtualisation and ensures that multiple nodes can share the same GPU. In this paper we test the feasibility of the rCUDA framework on a real-world application employed in the financial risk industry that can benefit from AaaS in the production setting. The results confirm the feasibility of rCUDA and highlight that rCUDA achieves similar performance compared to CUDA, provides consistent results, and more importantly, allows for a single application to benefit from all the GPUs available in the cluster without loosing efficiency.

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Section: A Financial Risk Case Studymentioning

confidence: 99%

Acceleration-as-a-Service: Exploiting Virtualised GPUs for a Financial Application

Varghese

Prades

Reaño

et al. 2015

2015 IEEE 11th International Conference on E-Science

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“…FPGA or ASIC based accelerator architecture works faster than accelerators with standard shared memories. Intel Xeon Phi and NVIDIA GPU achieve high computational performance in multi‐core processing systems [54].…”

Section: Introductionmentioning

confidence: 99%

Survey on memory management techniques in heterogeneous computing systems

Hazarika

Poddar

Rahaman

2020

IET Computers & Digital Techniques

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A major issue faced by data scientists today is how to scale up their processing infrastructure to meet the challenge of big data and high-performance computing (HPC) workloads. With today's HPC domain, it is required to connect multiple graphics processing units (GPUs) to accomplish large-scale parallel computing along with CPUs. Data movement between the processor and on-chip or off-chip memory creates a major bottleneck in overall system performance. The CPU/GPU processes all the data on a computer's memory and hence the speed of the data movement to/from memory and the size of the memory affect computer speed. During memory access by any processing element, the memory management unit (MMU) controls the data flow of the computer's main memory and impacts the system performance and power. Change in dynamic random access memory (DRAM) architecture, integration of memory-centric hardware accelerator in the heterogeneous system and Processing-in-Memory (PIM) are the techniques adopted from all the available shared resource management techniques to maximise the system throughput. This survey study presents an analysis of various DRAM designs and their performances. The authors also focus on the architecture, functionality, and performance of different hardware accelerators and PIM systems to reduce memory access time. Some insights and potential directions toward enhancements to existing techniques are also discussed. The requirement of fast, reconfigurable, self-adaptive memory management schemes in the high-speed processing scenario motivates us to track the trend. An effective MMU handles memory protection, cache control and bus arbitration associated with the processors.

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“…The rCUDA framework suits such an application because minimal changes need to be brought about to the production cluster and the acceleration required for the analysis is obtained as a service from a remote host. The analysis has previously been investigated in the context of many-core architectures [29], but we believe virtual GPUs can be a better option.…”

Section: Financial Risk Applicationmentioning

confidence: 99%

Multi-tenant virtual GPUs for optimising performance of a financial risk application

Prades

Varghese

Reaño

et al. 2017

Journal of Parallel and Distributed Computing

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Graphics Processing Units (GPUs) are becoming popular accelerators in modern High-Performance Computing (HPC) clusters. Installing GPUs on each node of the cluster is not efficient resulting in high costs and power consumption as well as underutilisation of the accelerator. The research reported in this paper is motivated towards the use of few physical GPUs by providing cluster nodes access to remote GPUs on-demand for a financial risk application. We hypothesise that sharing GPUs between several nodes, referred to as multi-tenancy, reduces the execution time and energy consumed by an application. Two data transfer modes between the CPU and the GPUs, namely concurrent and sequential, are explored. The key result from the experiments is that multi-tenancy with few physical GPUs using sequential data transfers lowers the execution time and the energy consumed, thereby improving the overall performance of the application.

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The hardware accelerator debate: A financial risk case study using many-core computing

Cited by 6 publications

References 34 publications

Acceleration-as-a-Service: Exploiting Virtualised GPUs for a Financial Application

Acceleration-as-a-Service: Exploiting Virtualised GPUs for a Financial Application

Survey on memory management techniques in heterogeneous computing systems

Multi-tenant virtual GPUs for optimising performance of a financial risk application

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