Resource Efficient Single Precision Floating Point Multiplier Using Karatsuba Algorithm

In floating point arithmetic operations, multiplication is the most required operation for many signal processing and scientific applications. 24-bit length mantissa multiplication is involved to obtain the floating point multiplication final result for two given single precision floating point numbers. This mantissa multiplication plays the major role in the performance evaluation in respect of occupied area and propagation delay. This paper presents the design and analysis of single precision floating point multiplication using karatsuba algorithm with vedic multiplier with the considering of modified 2x1 multiplexers and modified 4:2 compressors in order to overcome the drawbacks in the existing techniques. Further, the performance analysis of single precision floating point multiplier is analyzed in terms of area and delay using Karatsuba Algorithm with different existing techniques such as 4x1 multiplexers and 3:2 compressors and modified techniques such as 2x1 multiplexers, 4:2 compressors. From the simulation results, it is observed that single precision floating point multiplication with karatsuba algorithm using modified 4:2 compressor with XOR-MUX logic provides better performance with efficient usage of resources such as area and delay than that of existing techniques. All the blocks involved for floating point multiplication are coded with Verilog and synthesized using Xilinx ISE Simulator

Distributed N-body Simulation on the Grid Using Dedicated Hardware

We present performance measurements of direct gravitational N -body simulation on the grid, with and without specialized (GRAPE-6) hardware. Our inter-continental virtual organization consists of three sites, one in Tokyo, one in Philadelphia and one in Amsterdam. We run simulations with up to 196608 particles for a variety of topologies. In many cases, high performance simulations over the entire planet are dominated by network bandwidth rather than latency. With this global grid of GRAPEs our calculation time remains dominated by communication over the entire range of N, which was limited due to the use of three sites. Increasing the number of particles will result in a more efficient execution. Based on these timings we construct and calibrate a model to predict the performance of our simulation on any grid infrastructure with or without GRAPE. We apply this model to predict the simulation performance on the Netherlands DAS-3 wide area computer. Equipping the DAS-3 with GRAPE-6Af hardware would achieve break-even between calculation and communication at a few million particles, resulting in a compute time of just over ten hours for 1 N -body time unit. Key words: high-performance computing, grid, N-body simulation, performance modellingComment: (in press) New Astronomy, 24 pages, 5 figure

A Memory Controller for FPGA Applications

As designers and researchers strive to achieve higher performance, field-programmable gate arrays (FPGAs) become an increasingly attractive solution. As coprocessors, FPGAs can provide application specific acceleration that cannot be matched by modern processors. Most of these applications will make use of large data sets, so achieving acceleration will require a capable interface to this data. The research in this thesis describes the design of a memory controller that is both efficient and flexible for FPGA applications requiring floating point operations. In particular, the benefits of certain design choices are explored, including: scalability, memory caching, and configurable precision. Results are given to prove the controller\u27s effectiveness and to compare various design trade-offs

Sparse Matrix Sparse Vector Multiplication using Parallel and Reconfigurable Computing

The purpose of this thesis is to provide analysis and insight into the implementation of sparse matrix sparse vector multiplication on a reconfigurable parallel computing platform. Common implementations of sparse matrix sparse vector multiplication are completed by unary processors or parallel platforms today. Unary processor implementations are limited by their sequential solution of the problem while parallel implementations suffer from communication delays and load balancing issues when preprocessing techniques are not used or unavailable. By exploiting the deficiencies in sparse matrix sparse vector multiplication on a typical unary processor as a strength of parallelism on a Field Programmable Gate Array (FPGA), the potential performance improvements and tradeoffs for shifting the operation to hardware assisted implementation will be evaluated. This will simply be accomplished through multiple collaborating processes designed on an FPGA

Towards Power- and Energy-Efficient Datacenters

As the Internet evolves, cloud computing is now a dominant form of computation in modern lives. Warehouse-scale computers (WSCs), or datacenters, comprising the foundation of this cloud-centric web have been able to deliver satisfactory performance to both the Internet companies and the customers. With the increased focus and popularity of the cloud, however, datacenter loads rise and grow rapidly, and Internet companies are in need of boosted computing capacity to serve such demand. Unfortunately, power and energy are often the major limiting factors prohibiting datacenter growth: it is often the case that no more servers can be added to datacenters without surpassing the capacity of the existing power infrastructure. This dissertation aims to investigate the issues of power and energy usage in a modern datacenter environment. We identify the source of power and energy inefficiency at three levels in a modern datacenter environment and provides insights and solutions to address each of these problems, aiming to prepare datacenters for critical future growth. We start at the datacenter-level and find that the peak provisioning and improper service placement in multi-level power delivery infrastructures fragment the power budget inside production datacenters, degrading the compute capacity the existing infrastructure can support. We find that the heterogeneity among datacenter workloads is key to address this issue and design systematic methods to reduce the fragmentation and improve the utilization of the power budget. This dissertation then narrow the focus to examine the energy usage of individual servers running cloud workloads. Especially, we examine the power management mechanisms employed in these servers and find that the coarse time granularity of these mechanisms is one critical factor that leads to excessive energy consumption. We propose an intelligent and low overhead solution on top of the emerging finer granularity voltage/frequency boosting circuit to effectively pinpoints and boosts queries that are likely to increase the tail distribution and can reap more benefit from the voltage/frequency boost, improving energy efficiency without sacrificing the quality of services. The final focus of this dissertation takes a further step to investigate how using a fundamentally more efficient computing substrate, field programmable gate arrays (FPGAs), benefit datacenter power and energy efficiency. Different from other types of hardware accelerations, FPGAs can be reconfigured on-the-fly to provide fine-grain control over hardware resource allocation and presents a unique set of challenges for optimal workload scheduling and resource allocation. We aim to design a set coordinated algorithms to manage these two key factors simultaneously and fully explore the benefit of deploying FPGAs in the highly varying cloud environment.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144043/1/hsuch_1.pd

Control Software for Reconfigurable Coprocessors

On-line data processing at the ATLAS general purpose particle detector, which is currently under construction at Geneva, generates demands on computing power that are difficult to satisfy with commodity CPU-based computers. One of the most demanding applications is the recognition of particle tracks that originate from B-quark decays. However, this and many others applications can benefit from parallel execution on field programmable gate arrays (FPGA). After the demonstration of accelerated track recognition with big FPGA-based custom computers, the development of FPGA based coprocessors started in the late 1990's. Applications of FPGA coprocessors are usually partitioned between the host and the tightly coupled coprocessor. The objective of the research that I present in this thesis was the development of software that mediates to applications the access to FPGA coprocessors. I used a software process based on iterative prototyping to cope with the expected changing requirements. Also, I used a strict bottom-up design to create classes that model devices on the coprocessors. Using these low-level classes, I developed tools which were used for bootstrapping, debugging, and firmware update of the coprocessors during their development and maintenance. Measurements show that the software overhead introduced by object-oriented programming and software layering is small. The software-support for six different coprocessors was partitioned into corresponding independent packages, which reuse a set of packages that provide common and basic functions. The steady evolution and use of the software during more than four years shows that the software is maintainable, adaptable, and usable