A Highly Accelerated Parallel Multi-GPU based Reconstruction Algorithm for Generating Accurate Relative Stopping Powers

Low-dose Proton Computed Tomography (pCT) is an evolving imaging modality that is used in proton therapy planning which addresses the range uncertainty problem. The goal of pCT is generating a 3D map of Relative Stopping Power (RSP) measurements with high accuracy within clinically required time frames. Generating accurate RSP values within the shortest amount of time is considered a key goal when developing a pCT software. The existing pCT softwares have successfully met this time frame and even succeeded this time goal, but requiring clusters with hundreds of processors. This paper describes a novel reconstruction technique using two Graphics Processing Unit (GPU) cores, such as is available on a single Nvidia P100. The proposed reconstruction technique is tested on both simulated and experimental datasets and on two different systems namely Nvidia K40 and P100 GPUs from IBM and Cray. The experimental results demonstrate that our proposed reconstruction method meets both the timing and accuracy with the benefit of having reasonable cost, and efficient use of power.Comment: IEEE NSS/MIC 201

A Resource Management Architecture for Metacomputing Systems

Metacomputing systems are intended to support remote and/or concurrent use of geographically distributed computational resources. Resource management in such systems is complicated by five concerns that do not typically arise in other situations: site autonomy and heterogeneous substrates at the resources, and application requirements for policy extensibility, co-allocation, and online control. We describe a resource management architecture that addresses these concerns. This architecture distributes the resource management problem among distinct local manager, resource broker, and resource co-allocator components and defines an extensible resource specification language to exchange information about requirements. We describe how these techniques have been implemented in the context of the Globus metacomputing toolkit and used to implement a variety of different resource management strategies. We report on our experiences applying our techniques in a large testbed, GUSTO, incorporating 15 sites, 330 computers, and 3600 processors

Cactus-G: Enabling High-Performance Simulation in Heterogeneous Distributed Computing Environments

Improvements in the performance of processors and networks means that it can be both feasible and interesting to treat collections of workstations, servers, clusters, and supercomputers as integrated computational resources or Grids. However, the highly heterogeneous and dynamic nature of such Grids makes application development extremely difficult. In this paper, we describe an architecture and prototype implementation for a Grid-enabled computational framework called Cactus-G. This framework integrates the Cactus simulation system with the MPICH-G2 Grid-enabled message passing library and in addition integrates a variety of specialized features to support efficient execution in Grid environments. In order to evaluate and demonstrate the effectiveness of this system, we are attempting a challenge computation involving a large astrophysics simulation distributed across multiple supercomputers at U.S. centers. In this extended abstract, we present preliminary results that suggest that this challenge computation is feasible; the final paper will present complete results

A Highly Accelerated Parallel Multi-GPU based Reconstruction Algorithm for Generating Accurate Relative Stopping Powers

Low-dose Proton Computed Tomography (pCT) is an evolving imaging modality that is used in proton therapy planning which addresses the range uncertainty problem. The goal of pCT is generating a 3D map of relative stopping power measurements with high accuracy within clinically required time frames. Generating accurate relative stopping power values within the shortest amount of time is considered a key goal when developing an image reconstruction software. The existing image reconstruction softwares have successfully met this time frame and even exceeded this time goal, but require clusters with hundreds of processors. This paper describes a novel reconstruction technique using two graphics processing unit devices. The proposed reconstruction technique is tested on both simulated and experimental datasets and on two different systems namely Nvidia K40 and P100 graphics processing units from IBM and Cray. The experimental results demonstrate that our proposed reconstruction method meets both the timing and accuracy with the benefit of having reasonable cost and efficient use of power