Exploring Novel Parallelization Technologies for 3-D Imaging Applications

Rivera, Daniel; Schaa, Dana; Moffie, Micha; Kaeli, David

doi:10.1109/sbac-pad.2007.26

Cited by 9 publications

(6 citation statements)

References 28 publications

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“…Using GPUs for general purpose scientific computing has allowed a range of challenging problems to be solved faster and has enabled researchers to study larger (e.g., finer-grained) data sets [3], [7], [16], [18], [23]. In [21], Ryoo et.…”

Section: Related Workmentioning

confidence: 99%

“…Image Reconstruction: This application is a medical imaging algorithm that uses tomography to reconstruct a three-dimensional volume from multiple two-dimensional X-ray views [18]. The X-ray views and volume slices are independent and are divided between the GPUs.…”

Section: Applicationsmentioning

confidence: 99%

“…General purpose GPU (GPGPU) programming has become the scientific computing platform of choice mainly due to the availability of standard C libraries using NVIDIA's CUDA programming interface running on NVIDIA GTX GPUs. Since its first release, a number of efforts have explored how to reap large performance gains on CUDA-enabled GPUs [3], [7], [16], [18], [20], [21], [23].…”

Section: Introductionmentioning

confidence: 99%

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Exploring the multiple-GPU design space

Schaa

Kaeli

2009

2009 IEEE International Symposium on Parallel &Amp; Distributed Processing

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Graphics Processing Units (GPUs) have been growing in popularity due to their impressive processing capabilities, and with general purpose programming languages such as NVIDIA's CUDA interface, are becoming the platform of choice in the scientific computing community. Previous studies that used GPUs focused on obtaining significant performance gains from execution on a single GPU. These studies employed low-level, architecture-specific tuning in order to achieve sizeable benefits over multicore CPU execution.In this paper, we consider the benefits of running on multiple (parallel) GPUs to provide further orders of performance speedup. Our methodology allows developers to accurately predict execution time for GPU applications while varying the number and configuration of the GPUs, and the size of the input data set. This is a natural next step in GPU computing because it allows researchers to determine the most appropriate GPU configuration for an application without having to purchase hardware, or write the code for a multiple-GPU implementation. When used to predict performance on six scientific applications, our framework produces accurate performance estimates (11% difference on average and 40% maximum difference in a single case) for a range of short and long running scientific programs.

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Section: Related Workmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exploring the multiple-GPU design space

Schaa

Kaeli

2009

2009 IEEE International Symposium on Parallel &Amp; Distributed Processing

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“…This landscape is rapidly changing as relatively cheap computer systems that deliver supercomputer-level performance can be assembled from commodity multicore chips available from Intel, AMD, and Nvidia. For example, the Intel Xeon X7560, which uses the Nehalem microarchitecture, has a peak performance of 144 GFLOPs (8 cores, each with a 4 wide SSE unit, running at 2.266 GHz) with a total power dissipation of 130 W. The AMD Radeon 6870 graphics processing unit (GPU) can deliver a peak performance of nearly 2 TFLOPs (960 stream processor cores running at 850 MHz) with a total power dissipation of 256 W. For some applications, including medical imaging, electronic design automation, physics simulations, and stock pricing models, GPUs present a more attractive option in terms of performance, with speedups of up to 300X over conventional x86 processors (CPUs) [13], [14], [21], [18], [16]. However, these speedups are not universal as they depend heavily on both the nature of the application as well as the performance optimizations applied by the programmer [12].…”

Section: Introductionmentioning

confidence: 99%

A Customized Processor for Energy Efficient Scientific Computing

Sethia

Dasika

Mudge

et al. 2012

IEEE Trans. Comput.

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Abstract-The rapid advancements in the computational capabilities of the graphics processing unit (GPU) as well as the deployment of general programming models for these devices have made the vision of a desktop supercomputer a reality. It is now possible to assemble a system that provides several TFLOPs of performance on scientific applications for the cost of a high-end laptop computer. While these devices have clearly changed the landscape of computing, there are two central problems that arise. First, GPUs are designed and optimized for graphics applications resulting in delivered performance that is far below peak for more general scientific and mathematical applications. Second, GPUs are power hungry devices that often consume 100-300 watts, which restricts the scalability of the solution and requires expensive cooling. To combat these challenges, this paper presents the PEPSC architecture-an architecture customized for the domain of data parallel dense matrix style scientific application where power efficiency is the central focus. PEPSC utilizes a combination of a 2D single-instruction multiple-data (SIMD) datapath, an intelligent dynamic prefetching mechanism, and a configurable SIMD control approach to increase execution efficiency over conventional GPUs. A single PEPSC core has a peak performance of 120 GFLOPs while consuming 2 W of power when executing modern scientific applications, which represents an increase in computation efficiency of more than 10X over existing GPUs.

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“…The AMD Radeon 6870 graphics processing unit (GPU) can deliver a peak performance of nearly 2 TFLOPs (960 stream processor cores running at 850 MHz) with a total power dissipation of 256 Watts. For some applications, including medical imaging, electronic design automation, physics simulations, and stock pricing models, GPUs present a more attractive option in terms of performance, with speedups of up to 300X over conventional x86 processors (CPUs) [11], [12], [19], [16], [14]. However, these speedups are not universal as they depend heavily on Peak performance and power characteristics of several highperformance commercial CPUs and GPUs are provided: ARM Cortex-A8, Intel Pentium M, Core 2, and Core i7; IBM Cell; Nvidia GTX 280, Tesla S1070, and Tesla C2050; and AMD/ATI Radeon 5850 and 6850.…”

Section: Introductionmentioning

confidence: 99%

PEPSC: A Power-Efficient Processor for Scientific Computing

Dasika

Sethia

Mudge

et al. 2011

2011 International Conference on Parallel Architectures and Compilation Techniques

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Abstract-The rapid advancements in the computational capabilities of the graphics processing unit (GPU) as well as the deployment of general programming models for these devices have made the vision of a desktop supercomputer a reality. It is now possible to assemble a system that provides several TFLOPs of performance on scientific applications for the cost of a highend laptop computer. While these devices have clearly changed the landscape of computing, there are two central problems that arise. First, GPUs are designed and optimized for graphics applications resulting in delivered performance that is far below peak for more general scientific and mathematical applications. Second, GPUs are power hungry devices that often consume 100-300 watts, which restricts the scalability of the solution and requires expensive cooling. To combat these challenges, this paper presents the PEPSC architecture -an architecture customized for the domain of data parallel scientific applications where powerefficiency is the central focus. PEPSC utilizes a combination of a two-dimensional single-instruction multiple-data (SIMD) datapath, an intelligent dynamic prefetching mechanism, and a configurable SIMD control approach to increase execution efficiency over conventional GPUs. A single PEPSC core has a peak performance of 120 GFLOPs while consuming 2W of power when executing modern scientific applications, which represents an increase in computation efficiency of more than 10X over existing GPUs.

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Exploring Novel Parallelization Technologies for 3-D Imaging Applications

Abstract: Multi-dimensional imaging techniques involve the processing of high resolution images commonly used in medi-

Cited by 9 publications

References 28 publications

Exploring the multiple-GPU design space

Exploring the multiple-GPU design space

A Customized Processor for Energy Efficient Scientific Computing

PEPSC: A Power-Efficient Processor for Scientific Computing

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