Mapping Iterative Medical Imaging Algorithm on Cell Accelerator

Algebraic reconstruction techniques require about half the number of projections as that of Fourier backprojection methods, which makes these methods safer in terms of required radiation dose. Algebraic reconstruction technique (ART) and its variant OS-SART (ordered subset simultaneous ART) are techniques that provide faster convergence with comparatively good image quality. However, the prohibitively long processing time of these techniques prevents their adoption in commercial CT machines. Parallel computing is one solution to this problem. With the advent of heterogeneous multicore architectures that exploit data parallel applications, medical imaging algorithms such as OS-SART can be studied to produce increased performance. In this paper, we map OS-SART on cell broadband engine (Cell BE). We effectively use the architectural features of Cell BE to provide an efficient mapping. The Cell BE consists of one powerPC processor element (PPE) and eight SIMD coprocessors known as synergetic processor elements (SPEs). The limited memory storage on each of the SPEs makes the mapping challenging. Therefore, we present optimization techniques to efficiently map the algorithm on the Cell BE for improved performance over CPU version. We compare the performance of our proposed algorithm on Cell BE to that of Sun Fire ×4600, a shared memory machine. The Cell BE is five times faster than AMD Opteron dual-core processor. The speedup of the algorithm on Cell BE increases with the increase in the number of SPEs. We also experiment with various parameters, such as number of subsets, number of processing elements, and number of DMA transfers between main memory and local memory, that impact the performance of the algorithm

Cloud Resource Provisioning to Extend the Capacity of Local Resources in the Presence of Failures

Abstract—In this paper, we investigate Cloud computing re-source provisioning to extend the computing capacity of local clusters in the presence of failures. We consider three steps in the resource provisioning including resource brokering, dispatch sequences, and scheduling. The proposed brokering strategy is based on the stochastic analysis of routing in distributed parallel queues and takes into account the response time of the Cloud provider and the local cluster while considering computing cost of both sides. Moreover, we propose dispatching with probabilistic and deterministic sequences to redirect requests to the resource providers. We also incorporate checkpointing in some well-known scheduling algorithms to provide a fault-tolerant environment. We propose two cost-aware and failure-aware provisioning poli-cies that can be utilized by an organization that operates a cluster managed by virtual machine technology and seeks to use resources from a public Cloud provider. Simulation results demonstrate that the proposed policies improve the response time of users ’ requests by a factor of 4.10 under a moderate load with a limited cost on a public Cloud

A zone-based traffic assignment algorithm for scalable congestion reduction

Traffic assignment networks are networks with pseudo-static behavior: the network topology is constant but the cost of each edge changes in real-time. Extensive work has been completed in the literature to develop efficient traffic assignment algorithms in order to reduce traffic congestion. While some of these algorithms have proven to be effective, little attention has been paid to the matter of scalability in traffic networks. In this paper, we use zones to develop a hybrid approach to traffic assignment. We divide a traffic network into zones where the path within each zone is proactively stored, and paths between zones are reactively evaluated. This reduces the cost of route discovery. Using the Simulator of Urban MObility (SUMO), experiments were conducted to compare the zone-based system coined Z-BAR against a zone-free system. Between Z-BAR and a zone-free system, initial results showed Z-BAR introduces a speedup factor of up to 1.22

Enhancing performance of failure-prone clusters by adaptive provisioning of cloud resources

In this paper, we investigate Cloud computing resource provisioning to extend the computing capacity of local clusters in the presence of failures. We consider three steps in the resource provisioning including resource brokering, dispatch sequences, and scheduling. The proposed brokering strategy is based on the stochastic analysis of routing in distributed parallel queues and takes into account the response time of the Cloud provider and the local cluster while considering computing cost of both sides. Moreover, we propose dispatching with probabilistic and deterministic sequences to redirect requests to the resource providers. We also incorporate checkpointing in some well-known scheduling algorithms to provide a fault-tolerant environment. We propose two cost-aware and failure-aware provisioning policies that can be utilized by an organization that operates a cluster managed by virtual machine technology, and seeks to use resources from a public Cloud provider. Simulation results demonstrate that the proposed policies improve the response time of users’ requests by a factor of 4.10 under a moderate load with a limited cost on a public Cloud