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Kang, YixiuImplementation of Forward and Reverse Mode Automatic Differentiation for GNU Octave Applications
Master of Science (MS), Ohio University, 2003, Electrical Engineering & Computer Science (Engineering and Technology)

In this work, we present two C/C++ implementations of general purpose automatic differentiation (AD) for GNU Octave applications: FAD for forward mode AD and LogAD for reverse mode AD with bisection checkpointing. Both FAD and LogAD accept functions written in the GNU Octave language and work in the Octave environment via dynamically linked functions. FAD evaluates the product of the Jacobian of the input function and an arbitrary vector in time and space that are proportional to the time and space used by the original function. LogAD evaluates the product of an arbitrary vector and the Jacobian of the input function via a checkpointing approach first proposed by Griewank in 1992.

Committee:

David Juedes (Advisor)

Subjects:

Computer Science

Keywords:

Automatic Differentiation (AD); Forward Mode Automatic Differentiation; Reverse Mode Automatic Differentiation; Checkpointing; Scientific Computing; Computational Mathematics

Bas, Erdeniz OzgunLoad-Balancing Spatially Located Computations using Rectangular Partitions
Master of Science, The Ohio State University, 2011, Computer Science and Engineering
Distributing spatially located heterogeneous workloads is an important problem in parallel scientific computing. Particle-in-cell simulators, ray tracing and partial differential equations are some of the applications with spatially located workload. We investigate the problem of partitioning such workloads (represented as a matrix of non-negative integers) into rectangles, such that the load of the most loaded rectangle (processor) is minimized. Since finding the optimal arbitrary rectangle-based partition is an NP-hard problem, we investigate particular classes of solutions: rectilinear, jagged and hierarchical. We present a new class of solutions called m-way jagged partitions, propose new optimal algorithms for m-way jagged partitions and hierarchical partitions, propose new heuristic algorithms, and provide worst case performance analyses for some existing and new heuristics. Balancing the load does not guarantee to minimize the total runtime of an application. In order to achieve that, one must also take into account the communication cost. Rectangle shaped partitioning inherently keeps communications small, yet one should proactively minimize them. The algorithms we propose are tested in simulation on a wide set of instances and compared to state of the art algorithm. Results show that m-way jagged partitions are low in total communication cost and practical to use.

Committee:

Umit V. Catalyurek, PhD (Advisor); Radu Teodorescu, PhD (Committee Member)

Subjects:

Computer Science

Keywords:

matrix partitioning; load balancing; rectangular partitioning; scientific computing; high performance computing; parallel computing

Tirukkovalur, SravyaA Global Address Space Approach to Automated Data Management for Parallel Quantum Monte Carlo Applications
Master of Science, The Ohio State University, 2011, Computer Science and Engineering

Quantum Monte Carlo is a large class of computer algorithms that simulate quantum systems with the idea of solving the quantum many-body problem. Typical parallel quantum Monte Carlo(QMC) applications use very large spline interpolation tables that are unmodified after initialization. Although only a small fraction of the table may be accessed by each parallel thread/process in a window of execution, the accesses are quite random. Hence current implementations of these methods typically use replicated copies of the entire interpolation table at each node of a parallel computer. This limits scalability since increasing the number of processors does not enable larger systems to be run.

In this thesis, we take an automated data management approach which enables existing QMC codes to be adapted with minimal changes to significantly enhance the range of problem sizes that can be run. We primarily use Global Arrays partitioned global address space model to provide efficient distributed, shared storage and further the implementation is optimized by intelligent replication, locality, and data reuse management mechanisms. A transparent software caching mechanism is designed and built on the Global Arrays PGAS (Partitioned Global Address Space) programming model, to enable QMC codes to overcome their current memory limitations in running large-scale simulations. The new GA read-cache (GRC) has been used to enhance the scalability of QWalk, one of the popular QMC applications.

Committee:

Dr. Sadayappan P (Advisor); Dr. Srinivasan Parthasarathy (Committee Member)

Subjects:

Computer Science

Keywords:

PGAS ; QMC; Scientific Computing; Global Arrays, Quatum Monte Carlo;Software Cache;

Jamaliannasrabadi, SabaHigh Performance Computing as a Service in the Cloud Using Software-Defined Networking
Master of Science (MS), Bowling Green State University, 2015, Computer Science
Benefits of Cloud Computing (CC) such as scalability, reliability, and resource pooling have attracted scientists to deploy their High Performance Computing (HPC) applications on the Cloud. Nevertheless, HPC applications can face serious challenges on the cloud that could undermine the gained benefit, if care is not taken. This thesis targets to address the shortcomings of the Cloud for the HPC applications through a platform called HPC as a Service (HPCaaS). Further, a novel scheme is introduced to improve the performance of HPC task scheduling on the Cloud using the emerging technology of Software-Defined Networking (SDN). The research introduces “ASETS: A SDN-Empowered Task Scheduling System” as an elastic platform for scheduling HPC tasks on the cloud. In addition, a novel algorithm called SETSA is developed as part of the ASETS architecture to manage the scheduling task of the HPCaaS platform. The platform monitors the network bandwidths to take advantage of the changes when submitting tasks to the virtual machines. The experiments and benchmarking of HPC applications on the Cloud identified the virtualization overhead, cloud networking, and cloud multi-tenancy as the primary shortcomings of the cloud for HPC applications. A private Cloud Test Bed (CTB) was set up to evaluate the capabilities of ASETS and SETSA in addressing such problems. Subsequently, Amazon AWS public cloud was used to assess the scalability of the proposed systems. The obtained results of ASETS and SETSA on both private and public cloud indicate significant performance improvement of HPC applications can be achieved. Furthermore, the results suggest that proposed system is beneficial both to the cloud service providers and the users since ASETS performs better the degree of multi-tenancy increases. The thesis also proposes SETSAW (SETSA Window) as an improved version of SETSA algorism. Unlike other proposed solutions for HPCaaS which have either optimized the cloud to make it more HPC-friendly, or required adjusting HPC applications to make them more cloud-friendly, ASETS tends to provide a platform for existing cloud systems to improve the performance of HPC applications.

Committee:

Hassan Rajaei, Ph.D (Advisor); Robert Green, Ph.D (Committee Member); Jong Kwan Lee, Ph.D (Committee Member)

Subjects:

Computer Engineering; Computer Science; Technology

Keywords:

High Performance Computing; HPC; Cloud Computing; Scientific Computing; HPCaaS; Software Defined Networking; SDN; Cloud Networking; Virtualization