Computer and Systems Architecture Commons^™

Unconventional Computation Including Quantum Computation, Bruce J. Maclennan

Faculty Publications and Other Works -- EECS

Unconventional computation (or non-standard computation) refers to the use of non-traditional technologies and computing paradigms. As we approach the limits of Moore’s Law, progress in computation will depend on going beyond binary electronics and on exploring new paradigms and technologies for information processing and control. This book surveys some topics relevant to unconventional computation, including the definition of unconventional computations, the physics of computation, quantum computation, DNA and molecular computation, and analog computation. This book is the content of a course taught at UTK.

Automated Program Profiling And Analysis For Managing Heterogeneous Memory Systems, Adam Palmer Howard

Masters Theses

Many promising memory technologies, such as non-volatile, storage-class memories and high-bandwidth, on-chip RAMs, are beginning to emerge. Since each of these new technologies present tradeoffs distinct from conventional DRAMs, next-generation systems are likely to include multiple tiers of memory storage, each with their own type of devices. To efficiently utilize the available hardware, such systems will need to alter their data management strategies to consider the performance and capabilities provided by each tier.

This work explores a variety of cross-layer strategies for managing application data in heterogeneous memory systems. We propose different program profiling-based techniques to automatically partition program allocation …

Context-Sensitive Auto-Sanitization For Php, Jared M. Smith, Richard J. Connor, David P. Cunningham, Kyle G. Bashour, Walter T. Work

Chancellor’s Honors Program Projects

No abstract provided.

Achieving High Reliability And Efficiency In Maintaining Large-Scale Storage Systems Through Optimal Resource Provisioning And Data Placement, Lipeng Wan

Doctoral Dissertations

With the explosive increase in the amount of data being generated by various applications, large-scale distributed and parallel storage systems have become common data storage solutions and been widely deployed and utilized in both industry and academia. While these high performance storage systems significantly accelerate the data storage and retrieval, they also bring some critical issues in system maintenance and management. In this dissertation, I propose three methodologies to address three of these critical issues.

First, I develop an optimal resource management and spare provisioning model to minimize the impact brought by component failures and ensure a highly operational experience …

Programming Dense Linear Algebra Kernels On Vectorized Architectures, Jonathan Lawrence Peyton

Masters Theses

The high performance computing (HPC) community is obsessed over the general matrix-matrix multiply (GEMM) routine. This obsession is not without reason. Most, if not all, Level 3 Basic Linear Algebra Subroutines (BLAS) can be written in terms of GEMM, and many of the higher level linear algebra solvers' (i.e., LU, Cholesky) performance depend on GEMM's performance. Getting high performance on GEMM is highly architecture dependent, and so for each new architecture that comes out, GEMM has to be programmed and tested to achieve maximal performance. Also, with emergent computer architectures featuring more vector-based and multi to many-core processors, GEMM performance …

Exploring Computational Chemistry On Emerging Architectures, David Dewayne Jenkins

Doctoral Dissertations

Emerging architectures, such as next generation microprocessors, graphics processing units, and Intel MIC cards, are being used with increased popularity in high performance computing. Each of these architectures has advantages over previous generations of architectures including performance, programmability, and power efficiency. With the ever-increasing performance of these architectures, scientific computing applications are able to attack larger, more complicated problems. However, since applications perform differently on each of the architectures, it is difficult to determine the best tool for the job. This dissertation makes the following contributions to computer engineering and computational science. First, this work implements the computational chemistry variational …

Parallel For Loops On Heterogeneous Resources, Frederick Edward Weber

Doctoral Dissertations

In recent years, Graphics Processing Units (GPUs) have piqued the interest of researchers in scientific computing. Their immense floating point throughput and massive parallelism make them ideal for not just graphical applications, but many general algorithms as well. Load balancing applications and taking advantage of all computational resources in a machine is a difficult challenge, especially when the resources are heterogeneous. This dissertation presents the clUtil library, which vastly simplifies developing OpenCL applications for heterogeneous systems. The core focus of this dissertation lies in clUtil's ParallelFor construct and our novel PINA scheduler which can efficiently load balance work onto multiple …

Dynamic Task Execution On Shared And Distributed Memory Architectures, Asim Yarkhan

Doctoral Dissertations

Multicore architectures with high core counts have come to dominate the world of high performance computing, from shared memory machines to the largest distributed memory clusters. The multicore route to increased performance has a simpler design and better power efficiency than the traditional approach of increasing processor frequencies. But, standard programming techniques are not well adapted to this change in computer architecture design.

In this work, we study the use of dynamic runtime environments executing data driven applications as a solution to programming multicore architectures. The goals of our runtime environments are productivity, scalability and performance. We demonstrate productivity by …