Sanjay Ranka

Leaf sequencing algorithms for segmented multileaf collimation.

The delivery of intensity-modulated radiation therapy (IMRT) with a multileaf collimator (MLC) requires the conversion of a radiation fluence map into a leaf sequence file that controls the movement of the MLC during radiation delivery. It is imperative that the fluence map delivered using the leaf sequence file is as close as possible to the fluence map generated by the dose optimization algorithm, while satisfying hardware constraints of the delivery system. Optimization of the leaf sequencing algorithm has been the subject of several recent investigations. In this work, we present a systematic study of the optimization of leaf sequencing algorithms for segmental multileaf collimator beam delivery and provide rigorous mathematical proofs of optimized leaf sequence settings in terms of monitor unit (MU) efficiency under most common leaf movement constraints that include minimum leaf separation constraint and leaf interdigitation constraint. Our analytical analysis shows that leaf sequencing based on unidirectional movement of the MLC leaves is as MU efficient as bidirectional movement of the MLC leaves.

Using game theory for scheduling tasks on multi-core processors for simultaneous optimization of performance and energy

Multi-core processors are beginning to revolutionize the landscape of high-performance computing. In this paper, we address the problem of power-aware scheduling/mapping of tasks onto heterogeneous and homogeneous multi-core processor architectures. The objective of scheduling is to minimize the energy consumption as well as the makespan of computationally intensive problems. The multi- objective optimization problem is not properly handled by conventional approaches that try to maximize a single objective. Our proposed solution is based on game theory. We formulate the problem as a cooperate game. Although we can guarantee the existence of a Bargaining Point in this problem, the classical cooperative game theoretical techniques such as the Nash axiomatic technique cannot be used to identify the Bargaining Point due to low convergence rates and high complexity. Hence, we transform the problem to a max-max-min problem such that it can generate solutions with fast turnaround time.

Policy based scheduling for simple quality of service in grid computing

Summary form only given. We discuss a novel framework for policy based scheduling in resource allocation of grid computing. The framework has several features. First, the scheduling strategy can control the request assignment to grid resources by adjusting usage accounts or request priorities. Second, Efficient resource management is achieved by assigning usage quotas to intended users. Third, the scheduling method supports reservation based grid resource allocation. Fourth, quality of service feature allows special privileges to various classes of requests. Experimental results demonstrate the usefulness of the framework.

Memory hierarchy management for iterative graph structures

The increasing gap in processor and memory speeds has forced microprocessors to rely on deep cache hierarchies to keep the processors from starving for data. For many applications, this results in a wide disparity between sustained and peak achievable speed. Applications need to be tuned to processor and memory system architectures for cache locality, memory layout and data prefetch and reuse. In this paper we investigate optimizations for unstructured iterative applications in which the computational structure remains static or changes only slightly through iterations. Our methods reorganize the data elements to obtain better memory system performance without modifying code fragments. Our experimental results show that the overall time can be reduced significantly using our optimizations. Further, the overhead of our methods is small enough that they are applicable even if the computational structure does nor substantially change for tens of iterations.

Dynamic cache reconfiguration and partitioning for energy optimization in real-time multi-core systems

Multicore architectures, especially chip multi-processors, have been widely acknowledged as a successful design paradigm. Existing approaches primarily target application-driven partitioning of the shared cache to alleviate inter-core cache interference so that both performance and energy efficiency are improved. Dynamic cache reconfiguration is a promising technique in reducing energy consumption of the cache subsystem for uniprocessor systems. In this paper, we present a novel energy optimization technique which employs both dynamic reconfiguration of private caches and partitioning of the shared cache for multicore systems with real-time tasks. Our static profiling based algorithm is designed to judiciously find beneficial cache configurations (of private caches) for each task as well as partition factors (of the shared cache) for each core so that the energy consumption is minimized while task deadline is satisfied. Experimental results using real benchmarks demonstrate that our approach can achieve 29.29% energy saving on average compared to systems employing only cache partitioning.

Bandwidth Scheduling and Path Computation Algorithms for Connection-Oriented Networks

There has been an increasing number of network deployments that provide dedicated connections through on-demand and in-advance scheduling in support of high- performance applications. We describe algorithms for scheduling and path computations needed for dedicated bandwidth connections for fixed-slot, highest available bandwidth in a given slot, first available slot, and all-available slots computations. These algorithms for bandwidth scheduling are based on extending the classical breadth-first search, Dijkstra, and Bellman-Ford algorithms. We describe a bandwidth management system for UltraScience Net that incorporates implementations of these algorithms.

Distance-based clustering of CGH data

MOTIVATION We consider the problem of clustering a population of Comparative Genomic Hybridization (CGH) data samples. The goal is to develop a systematic way of placing patients with similar CGH imbalance profiles into the same cluster. Our expectation is that patients with the same cancer types will generally belong to the same cluster as their underlying CGH profiles will be similar. RESULTS We focus on distance-based clustering strategies. We do this in two steps. (1) Distances of all pairs of CGH samples are computed. (2) CGH samples are clustered based on this distance. We develop three pairwise distance/similarity measures, namely raw, cosine and sim. Raw measure disregards correlation between contiguous genomic intervals. It compares the aberrations in each genomic interval separately. The remaining measures assume that consecutive genomic intervals may be correlated. Cosine maps pairs of CGH samples into vectors in a high-dimensional space and measures the angle between them. Sim measures the number of independent common aberrations. We test our distance/similarity measures on three well known clustering algorithms, bottom-up, top-down and k-means with and without centroid shrinking. Our results show that sim consistently performs better than the remaining measures. This indicates that the correlation of neighboring genomic intervals should be considered in the structural analysis of CGH datasets. The combination of sim with top-down clustering emerged as the best approach. AVAILABILITY All software developed in this article and all the datasets are available from the authors upon request. CONTACT juliu@cise.ufl.edu.

Algorithms for optimal sequencing of dynamic multileaf collimators.

Dynamic multileaf collimator (DMLC) intensity modulated radiation therapy (IMRT) is used to deliver intensity modulated beams using a multileaf collimator (MLC), with the leaves in motion. DMLC-IMRT requires the conversion of a radiation intensity map into a leaf sequence file that controls the movement of the MLC while the beam is on. It is imperative that the intensity map delivered using the leaf sequence file be as close as possible to the intensity map generated by the dose optimization algorithm, while satisfying hardware constraints of the delivery system. Optimization of the leaf-sequencing algorithm has been the subject of several recent investigations. In this work, we present a systematic study of the optimization of leaf-sequencing algorithms for dynamic multileaf collimator beam delivery and provide rigorous mathematical proofs of optimized leaf sequence settings in terms of monitor unit (MU) efficiency under the most common leaf movement constraints that include leaf interdigitation constraint. Our analytical analysis shows that leaf sequencing based on unidirectional movement of the MLC leaves is as MU efficient as bi-directional movement of the MLC leaves.

Parallel incremental graph partitioning

Partitioning graphs into equally large groups of nodes while minimizing the number of edges between different groups is an extremely important problem in parallel computing. For instance, efficiently parallelizing several scientific and engineering applications requires the partitioning of data or tasks among processors such that the computational load on each node is roughly the same, while communication is minimized. Obtaining exact solutions is computationally intractable, since graph partitioning is NP-complete. For a large class of irregular and adaptive data parallel applications (such as adaptive graphs), the computational structure changes from one phase to another in an incremental fashion. In incremental graph-partitioning problems the partitioning of the graph needs to be updated as the graph changes over time; a small number of nodes or edges may be added or deleted at any given instant. In this paper, we use a linear programming-based method to solve the incremental graph-partitioning problem. All the steps used by our method are inherently parallel and hence our approach can be easily parallelized. By using an initial solution for the graph partitions derived from recursive spectral bisection-based methods, our methods can achieve repartitioning at considerably lower cost than can be obtained by applying recursive spectral bisection. Further, the quality of the partitioning achieved is comparable to that achieved by applying recursive spectral bisection to the incremental graphs from scratch.

Advance Reservations and Scheduling for Bulk Transfers in Research Networks

Data-intensive e-science collaborations often require the transfer of large files with predictable performance. To meet this need, we design novel admission control (AC) and scheduling algorithms for bulk data transfer in research networks for e-science. Due to their small sizes, the research networks can afford a centralized resource management platform. In our design, each bulk transfer job request, which can be made in advance to the central network controller, specifies a start time and an end time. If admitted, the network guarantees to complete the transfer before the end time. However, there is flexibility in how the actual transfer is carried out, that is, in the bandwidth assignment on each allowed path of the job on each time interval, and it is up to the scheduling algorithm to decide this. To improve the network resource utilization or lower the job rejection ratio, the network controller solves optimization problems in making AC and scheduling decisions. Our design combines the following elements into a cohesive optimization-based framework: advance reservations, multipath routing, and bandwidth reassignment via periodic reoptimization. We evaluate our algorithm in terms of both network efficiency and the performance level of individual transfer. We also evaluate the feasibility of our scheme by studying the algorithm execution time.