A versatile programming model for dynamic task scheduling on cluster computers

This dissertation studies the development of application programs for parallel and distributed computer systems, especially PC clusters. A methodology is proposed to increase the efficiency of code development, the productivity of programmers and enhance performance of executing the developed programs on PC clusters while facilitating improvement of scalability and code portability of these programs. A new programming model, named the Super-Programming Model (SPM), is created. Programs are developed assuming an instruction set architecture comprised of SuperInstructions (SIs). SPM models the target system as a large Virtual Machine (VM); VM contains functional units which are underlain with sub-computer systems and SIs are implemented with codes. When these functional units execute SIs, their codes will run on member computers to perform the corresponding operations. This approach resembles the process of designing instruction sets for microprocessors but the VM employs much coarser instructions and data structures. SIs use Super-Data Blocks (SDBs) as their operands. Each SI is assigned to a single member computer and is indivisible (i.e., its implementation is not interrupted for I/O). SIs have predictable execution times because SDB sizes are limited by predefined thresholds. These qualities of SIs help dynamic load balancing. Employing software to implement instructions makes this approach more flexible. The developed programs fit to architectures of cluster systems better. SPM provides mechanisms, such as dynamic load balancing, to assure the efficient execution of programs. The vast majority of current programming models lack such mechanisms for distributed environments that suffer from long communication latencies. Since SPM employs coarse-grain tasks, the overall management overhead is small. SDB access can often overlap the execution of other SIs; a cache system further decreases average memory latencies. Since all SDBs are virtual entities, with the runtime system support, they can be accessed in parallel and efficiently minimizes additional constraints to parallelism from underlying computer systems. In this research, a reference implementation of VM has been developed. A performance estimation model is developed that takes these features into account. Finally, the definition of scalability for parallel/distributed processing is refined to represent a multi-dimensional entity. Sample cases are analyzed

MAGDA: A Mobile Agent based Grid Architecture

Mobile agents mean both a technology and a programming paradigm. They allow for a flexible approach which can alleviate a number of issues present in distributed and Grid-based systems, by means of features such as migration, cloning, messaging and other provided mechanisms. In this paper we describe an architecture (MAGDA – Mobile Agent based Grid Architecture) we have designed and we are currently developing to support programming and execution of mobile agent based application upon Grid systems

Designing SSI clusters with hierarchical checkpointing and single I/O space

Adopting a new hierarchical checkpointing architecture, the authors develop a single I/O address space for building highly available clusters of computers. They propose a systematic approach to achieving a single system image by integrating existing middleware support with the newly developed features.published_or_final_versio

Structure-Aware Dynamic Scheduler for Parallel Machine Learning

Training large machine learning (ML) models with many variables or parameters can take a long time if one employs sequential procedures even with stochastic updates. A natural solution is to turn to distributed computing on a cluster; however, naive, unstructured parallelization of ML algorithms does not usually lead to a proportional speedup and can even result in divergence, because dependencies between model elements can attenuate the computational gains from parallelization and compromise correctness of inference. Recent efforts toward this issue have benefited from exploiting the static, a priori block structures residing in ML algorithms. In this paper, we take this path further by exploring the dynamic block structures and workloads therein present during ML program execution, which offers new opportunities for improving convergence, correctness, and load balancing in distributed ML. We propose and showcase a general-purpose scheduler, STRADS, for coordinating distributed updates in ML algorithms, which harnesses the aforementioned opportunities in a systematic way. We provide theoretical guarantees for our scheduler, and demonstrate its efficacy versus static block structures on Lasso and Matrix Factorization

Cloud Computing: The Future of IT Industry

As a result of the research processing in the computing field, a new computing model appeared based on the development of many computing models such as parallel computing, distributed computing, and grid computing. Many normal distributed computers collaborate of achieve a function like a super computer. The computation will be assigned to this super computer rather than local computer or remote server. This is the basic concept of cloud computing. However, there is a new implementation of cloud computing was introduced based on using the internet millions of computers connected to a super cloud. Cloud computing has several advantages such as; user does not need to worry about how the cloud runs, viruses, maintenance, etc. We would expect that cloud computing is going to reshape the IT industry. In this paper we discuss cloud computing from different angles such as concept, characteristics and classifications of cloud computing

Performance measurement and analysis of PC based cluster server using SET of Architecture and modeling a scalable High performance cluster

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