Exploration and Design of Power-Efficient Networked Many-Core Systems

Multiprocessing is a promising solution to meet the requirements of near future applications. To get full benefit from parallel processing, a manycore system needs efficient, on-chip communication architecture. Networkon- Chip (NoC) is a general purpose communication concept that offers highthroughput, reduced power consumption, and keeps complexity in check by a regular composition of basic building blocks. This thesis presents power efficient communication approaches for networked many-core systems. We address a range of issues being important for designing power-efficient manycore systems at two different levels: the network-level and the router-level. From the network-level point of view, exploiting state-of-the-art concepts such as Globally Asynchronous Locally Synchronous (GALS), Voltage/ Frequency Island (VFI), and 3D Networks-on-Chip approaches may be a solution to the excessive power consumption demanded by today’s and future many-core systems. To this end, a low-cost 3D NoC architecture, based on high-speed GALS-based vertical channels, is proposed to mitigate high peak temperatures, power densities, and area footprints of vertical interconnects in 3D ICs. To further exploit the beneficial feature of a negligible inter-layer distance of 3D ICs, we propose a novel hybridization scheme for inter-layer communication. In addition, an efficient adaptive routing algorithm is presented which enables congestion-aware and reliable communication for the hybridized NoC architecture. An integrated monitoring and management platform on top of this architecture is also developed in order to implement more scalable power optimization techniques. From the router-level perspective, four design styles for implementing power-efficient reconfigurable interfaces in VFI-based NoC systems are proposed. To enhance the utilization of virtual channel buffers and to manage their power consumption, a partial virtual channel sharing method for NoC routers is devised and implemented. Extensive experiments with synthetic and real benchmarks show significant power savings and mitigated hotspots with similar performance compared to latest NoC architectures. The thesis concludes that careful codesigned elements from different network levels enable considerable power savings for many-core systems.Siirretty Doriast

Efficient Storage of Genomic Sequences in High Performance Computing Systems

ABSTRACT: In this dissertation, we address the challenges of genomic data storage in high performance computing systems. In particular, we focus on developing a referential compression approach for Next Generation Sequence data stored in FASTQ format files. The amount of genomic data available for researchers to process has increased exponentially, bringing enormous challenges for its efficient storage and transmission. General-purpose compressors can only offer limited performance for genomic data, thus the need for specialized compression solutions. Two trends have emerged as alternatives to harness the particular properties of genomic data: non-referential and referential compression. Non-referential compressors offer higher compression rations than general purpose compressors, but still below of what a referential compressor could theoretically achieve. However, the effectiveness of referential compression depends on selecting a good reference and on having enough computing resources available. This thesis presents one of the first referential compressors for FASTQ files. We first present a comprehensive analytical and experimental evaluation of the most relevant tools for genomic raw data compression, which led us to identify the main needs and opportunities in this field. As a consequence, we propose a novel compression workflow that aims at improving the usability of referential compressors. Subsequently, we discuss the implementation and performance evaluation for the core of the proposed workflow: a referential compressor for reads in FASTQ format that combines local read-to-reference alignments with a specialized binary-encoding strategy. The compression algorithm, named UdeACompress, achieved very competitive compression ratios when compared to the best compressors in the current state of the art, while showing reasonable execution times and memory use. In particular, UdeACompress outperformed all competitors when compressing long reads, typical of the newest sequencing technologies. Finally, we study the main aspects of the data-level parallelism in the Intel AVX-512 architecture, in order to develop a parallel version of the UdeACompress algorithms to reduce the runtime. Through the use of SIMD programming, we managed to significantly accelerate the main bottleneck found in UdeACompress, the Suffix Array Construction

Design Space Exploration for MPSoC Architectures

Multiprocessor system-on-chip (MPSoC) designs utilize the available technology and communication architectures to meet the requirements of the upcoming applications. In MPSoC, the communication platform is both the key enabler, as well as the key differentiator for realizing efficient MPSoCs. It provides product differentiation to meet a diverse, multi-dimensional set of design constraints, including performance, power, energy, reconfigurability, scalability, cost, reliability and time-to-market. The communication resources of a single interconnection platform cannot be fully utilized by all kind of applications, such as the availability of higher communication bandwidth for computation but not data intensive applications is often unfeasible in the practical implementation. This thesis aims to perform the architecture-level design space exploration towards efficient and scalable resource utilization for MPSoC communication architecture. In order to meet the performance requirements within the design constraints, careful selection of MPSoC communication platform, resource aware partitioning and mapping of the application play important role. To enhance the utilization of communication resources, variety of techniques such as resource sharing, multicast to avoid re-transmission of identical data, and adaptive routing can be used. For implementation, these techniques should be customized according to the platform architecture. To address the resource utilization of MPSoC communication platforms, variety of architectures with different design parameters and performance levels, namely Segmented bus (SegBus), Network-on-Chip (NoC) and Three-Dimensional NoC (3D-NoC), are selected. Average packet latency and power consumption are the evaluation parameters for the proposed techniques. In conventional computing architectures, fault on a component makes the connected fault-free components inoperative. Resource sharing approach can utilize the fault-free components to retain the system performance by reducing the impact of faults. Design space exploration also guides to narrow down the selection of MPSoC architecture, which can meet the performance requirements with design constraints.Siirretty Doriast

Biometrics

Biometrics-Unique and Diverse Applications in Nature, Science, and Technology provides a unique sampling of the diverse ways in which biometrics is integrated into our lives and our technology. From time immemorial, we as humans have been intrigued by, perplexed by, and entertained by observing and analyzing ourselves and the natural world around us. Science and technology have evolved to a point where we can empirically record a measure of a biological or behavioral feature and use it for recognizing patterns, trends, and or discrete phenomena, such as individuals' and this is what biometrics is all about. Understanding some of the ways in which we use biometrics and for what specific purposes is what this book is all about

SIMULATING SEISMIC WAVE PROPAGATION IN TWO-DIMENSIONAL MEDIA USING DISCONTINUOUS SPECTRAL ELEMENT METHODS

We introduce a discontinuous spectral element method for simulating seismic wave in 2- dimensional elastic media. The methods combine the flexibility of a discontinuous finite element method with the accuracy of a spectral method. The elastodynamic equations are discretized using high-degree of Lagrange interpolants and integration over an element is accomplished based upon the Gauss-Lobatto-Legendre integration rule. This combination of discretization and integration results in a diagonal mass matrix and the use of discontinuous finite element method makes the calculation can be done locally in each element. Thus, the algorithm is simplified drastically. We validated the results of one-dimensional problem by comparing them with finite-difference time-domain method and exact solution. The comparisons show excellent agreement

The Role of Distributed Computing in Big Data Science: Case Studies in Forensics and Bioinformatics

2014 - 2015The era of Big Data is leading the generation of large amounts of data, which require storage and analysis capabilities that can be only ad- dressed by distributed computing systems. To facilitate large-scale distributed computing, many programming paradigms and frame- works have been proposed, such as MapReduce and Apache Hadoop, which transparently address some issues of distributed systems and hide most of their technical details. Hadoop is currently the most popular and mature framework sup- porting the MapReduce paradigm, and it is widely used to store and process Big Data using a cluster of computers. The solutions such as Hadoop are attractive, since they simplify the transformation of an application from non-parallel to the distributed one by means of general utilities and without many skills. However, without any algorithm engineering activity, some target applications are not alto- gether fast and e cient, and they can su er from several problems and drawbacks when are executed on a distributed system. In fact, a distributed implementation is a necessary but not su cient condition to obtain remarkable performance with respect to a non-parallel coun- terpart. Therefore, it is required to assess how distributed solutions are run on a Hadoop cluster, and/or how their performance can be improved to reduce resources consumption and completion times. In this dissertation, we will show how Hadoop-based implementations can be enhanced by using carefully algorithm engineering activity, tuning, pro ling and code improvements. It is also analyzed how to achieve these goals by working on some critical points, such as: data local computation, input split size, number and granularity of tasks, cluster con guration, input/output representation, etc. i In particular, to address these issues, we choose some case studies coming from two research areas where the amount of data is rapidly increasing, namely, Digital Image Forensics and Bioinformatics. We mainly describe full- edged implementations to show how to design, engineer, improve and evaluate Hadoop-based solutions for Source Camera Identi cation problem, i.e., recognizing the camera used for taking a given digital image, adopting the algorithm by Fridrich et al., and for two of the main problems in Bioinformatics, i.e., alignment- free sequence comparison and extraction of k-mer cumulative or local statistics. The results achieved by our improved implementations show that they are substantially faster than the non-parallel counterparts, and re- markably faster than the corresponding Hadoop-based naive imple- mentations. In some cases, for example, our solution for k-mer statis- tics is approximately 30× faster than our Hadoop-based naive im- plementation, and about 40× faster than an analogous tool build on Hadoop. In addition, our applications are also scalable, i.e., execution times are (approximately) halved by doubling the computing units. Indeed, algorithm engineering activities based on the implementation of smart improvements and supported by careful pro ling and tun- ing may lead to a much better experimental performance avoiding potential problems. We also highlight how the proposed solutions, tips, tricks and insights can be used in other research areas and problems. Although Hadoop simpli es some tasks of the distributed environ- ments, we must thoroughly know it to achieve remarkable perfor- mance. It is not enough to be an expert of the application domain to build Hadop-based implementations, indeed, in order to achieve good performance, an expert of distributed systems, algorithm engi- neering, tuning, pro ling, etc. is also required. Therefore, the best performance depend heavily on the cooperation degree between the domain expert and the distributed algorithm engineer. [edited by Author]XIV n.s