Academic literature on the topic 'Computing clusters'

Shared clusters represent an excellent platform for the execution of parallel applications given their low price/performance ratio and the presence of cluster infrastructure in many organisations. The focus of recent research efforts are on parallelism management, transport and efficient access to resources, and making clusters easy to use. In this thesis, we examine reliable parallel computing on clusters. The aim of this research is to demonstrate the feasibility of developing an operating system facility providing transport fault tolerance using existing, enhanced and newly built operating system services for supporting parallel applications. In particular, we use existing process duplication and process migration services, and synthesise a group communications facility for use in a transparent checkpointing facility. This research is carried out using the methods of experimental computer science. To provide a foundation for the synthesis of the group communications and checkpointing facilities, we survey and review related work in both fields. For group communications, we examine the V Distributed System, the x-kernel and Psync, the ISIS Toolkit, and Horus. We identify a need for services that consider the placement of processes on computers in the cluster. For Checkpointing, we examine Manetho, KeyKOS, libckpt, and Diskless Checkpointing. We observe the use of remote computer memories for storing checkpoints, and the use of copy-on-write mechanisms to reduce the time to create a checkpoint of a process. We propose a group communications facility providing two sets of services: user-oriented services and system-oriented services. User-oriented services provide transparency and target application. System-oriented services supplement the user-oriented services for supporting other operating systems services and do not provide transparency. Additional flexibility is achieved by providing delivery and ordering semantics independently. An operating system facility providing transparent checkpointing is synthesised using coordinated checkpointing. To ensure a consistent set of checkpoints are generated by the facility, instead of blindly blocking the processes of a parallel application, only non-deterministic events are blocked. This allows the processes of the parallel application to continue execution during the checkpoint operation. Checkpoints are created by adapting process duplication mechanisms, and checkpoint data is transferred to remote computer memories and disk for storage using the mechanisms of process migration. The services of the group communications facility are used to coordinate the checkpoint operation, and to transport checkpoint data to remote computer memories and disk. Both the group communications facility and the checkpointing facility have been implemented in the GENESIS cluster operating system and provide proof-of-concept. GENESIS uses a microkernel and client-server based operating system architecture, and is demonstrated to provide an appropriate environment for the development of these facilities. We design a number of experiments to test the performance of both the group communications facility and checkpointing facility, and to provide proof-of-performance. We present our approach to testing, the challenges raised in testing the facilities, and how we overcome them. For group communications, we examine the performance of a number of delivery semantics. Good speed-ups are observed and system-oriented group communication services are shown to provide significant performance advantages over user-oriented semantics in the presence of packet loss. For checkpointing, we examine the scalability of the facility given different levels of resource usage and a variable number of computers. Low overheads are observed for checkpointing a parallel application. It is made clear by this research that the microkernel and client-server based cluster operating system provide an ideal environment for the development of a high performance group communications facility and a transparent checkpointing facility for generating a platform for reliable parallel computing on clusters.

Many problems faced in the engineering world are computationally intensive. Filtering using FIR (Finite Impulse Response) filters is an example to that. This thesis discusses the implementation of a fast, reconfigurable, and scalable FIR (Finite Impulse Response) digital filter. Constant coefficient multipliers and a Fast FIFO implementation are also discussed in connection with the FIR filter. This filter is used in two of its structures: the direct-form and the lattice structure. The thesis describes several configurations that can be created with the different components available and reports the testing results of these configurations.<br>Master of Science

The computing needs and the input and result datasets of modern scientific and enterprise applications are growing exponentially. To support such applications, High-Performance Computing (HPC) systems need to employ thousands of cores and innovative data management. At the same time, an emerging trend in designing HPC systems is to leverage specialized asymmetric multicores, such as IBM Cell and AMD Fusion APUs, and commodity computational accelerators, such as programmable GPUs, which exhibit excellent price to performance ratio as well as the much needed high energy efficiency. While such accelerators have been studied in detail as stand-alone computational engines, integrating the accelerators into large-scale distributed systems with heterogeneous computing resources for data-intensive computing presents unique challenges and trade-offs. Traditional programming and resource management techniques cannot be directly applied to many-core accelerators in heterogeneous distributed settings, given the complex and custom instruction sets architectures, memory hierarchies and I/O characteristics of different accelerators. In this dissertation, we explore the design space of using commodity accelerators, specifically IBM Cell and programmable GPUs, in distributed settings for data-intensive computing and propose an adaptive framework for programming and managing heterogeneous clusters. The proposed framework provides a MapReduce-based extended programming model for heterogeneous clusters, which distributes tasks between asymmetric compute nodes by considering workload characteristics and capabilities of individual compute nodes. The framework provides efficient data prefetching techniques that leverage general-purpose cores to stage the input data in the private memories of the specialized cores. We also explore the use of an advanced layered-architecture based software engineering approach and provide mixin-layers based reusable software components to enable easy and quick deployment of heterogeneous clusters. The framework also provides multiple resource management and scheduling policies under different constraints, e.g., energy-aware and QoS-aware, to support executing concurrent applications on multi-tenant heterogeneous clusters. When applied to representative applications and benchmarks, our framework yields significantly improved performance in terms of programming efficiency and optimal resource management as compared to conventional, hand-tuned, approaches to program and manage accelerator-based heterogeneous clusters.<br>Ph. D.