Large-scale Parallel Applications Research Articles

An Effective and Practical Performance Prediction Model for Parallel Computing on Nondedicated Heterogeneous NOW

Networks of workstations (NOW) are receiving increased attention as a viable platform for high performance parallel computations. Heterogeneity and time-sharing are two characteristics that distinguish the NOW systems from conventional multiprocessor/multicomputer systems which are homogeneous and dedicated. It is important to have a practical model for users to predict the execution times of large-scale parallel applications on nondedicated heterogeneous NOW. Another objective of this study is to provide insight into the dynamic performance of parallel computing and into the effects of program structures and system factors on such a platform. In this paper, we study performance predictions for parallel computing on nondedicated heterogeneous networks of workstations. Our approach is based on a two-level model. On the top level, a semideterministic task graph is used to capture the parallel execution behavior including the variances of communication and synchronization. On the bottom level, a discrete time model is used to quantify effects from NOW systems. An iterative process is used to determine the interactive effects between network contention and task execution. We validate the prediction model using experiments on a nondedicated heterogeneous NOW. The maximum differences between predicted results and measured results were less than 10% in most cases and 15% in the worst cases.

A caching model of operating system kernel functionality

Operating system design has had limited success in providing adequate application functionality and a poor record in avoiding excessive growth in size and complexity, especially with protected operating systems. Applications require far greater control over memory, I/O and processing resources to meet their requirements. For example, database transaction processing systems include their own "kernel" which can much better manage resources for the application than can the application-ignorant general-purpose conventional operating system mechanisms. Large-scale parallel applications have similar requirements. The same requirements arise with servers implemented outside the operating system kernel.In our research, we have been exploring the approach of making the operating system kernel a cache for active operating systems objects such as processes, address spaces and communication channels, rather than a complete manager of these objects. The resulting system is smaller than recent so-called micro-kernels, and also provides greater flexibility for applications, including real-time applications, database management systems and large-scale simulations. As part of this research, we have developed what we call a cache kernel, a new type of micro-kernel that supports operating system configurations across these dimensions.The cache kernel can also be regarded as providing a hardware adaptation layer (HAL) to operating system services rather than trying to just provide a key subset of OS services, as has been the common approach in previous micro-kernel work. However, in contrast to conventional HALs, the cache kernel is fault-tolerant because it is protected from the rest of the operating system (and applications), it is replicated in large-scale configurations and it includes audit and recovery mechanisms. A cache kernel has been implemented on a scalable shared-memory and networked multi-computer [2] hardware which provides architectural support for the cache kernel approach.Figure 1 illustrates a typical target configuration. There is an instance of the cache kernel per multi-processor module (MPM), each managing the processors, second-level cache and network interface of that MPM. The cache kernel executes out of PROM and local memory of the MPM, making it hardware-independent of the rest of the system except for power. That is, the separate cache kernels and MPMs fail independently. Operating system services are provided by application kernels, server kernels and conventional operating system emulation kernels in conjunction with privileged MPM resource managers (MRM) that execute on top of the cache kernel. These kernels may be in separate protected address spaces or a shared library within a sophisticated application address space. A system bus connects the MPMs to each other and the memory modules. A high-speed network interface per MPM connects this node to file servers and other similarly configured processing nodes. This overall design can be simplified for real-time applications and similar restricted scenarios. For example, with relatively static partitioning of resources, an embedded real-time application could be structured as one or more application spaces incorporating application kernels as shared libraries executing directly on top of the cache kernel.

The effect of a lagrange multiplier approach in MSC/NASTRAN on large-scale parallel applications

The paper investigates the effect of a Langrange multiplier solution in a parallel environment. An MSC/NASTRAN linear static solution problem with Lagrange multiplier method on a four-CPU parallel computer is analysed and solved.

Substructuring in MSC/NASTRAN for large scale parallel applications

The purpose of this paper is to describe basic principles and practical experiences with the substructuring capabilities of MSC/NASTRAN on theoretical benchmarks as well as large scale practical applications. The adaptability of the capabilities for parallel processing and the theoretical limitations are evaluated.

“Topologies”—distributed objects on multicomputers

Application programs written for large-scale multicomputers with interconnection structures known to the programmer (e.g., hypercubes or meshes) use complex communication structures for connecting the applications' parallel tasks. Such structures implement a wide variety of functions, including the exchange of data or control information relevant to the task computations and/or the communications required for task synchronization, message forwarding/filtering under program control, and so on.Topologyis a programming and operating system construct that allows programmers to describe and efficiently implement such functionality as distributed objects with well-defined operational interfaces. As with abstract data types, topologies may be reused by any application desiring their functionality. However, in contrast to other research in parallel or distributed object-based operating systems, internally, a topology may be an entirely distributed implementation of the object's functionality, consisting of a communication graph and type-specific computations, which are triggered by messages traversing the graph. Sample computations may perform additions or minimizations of the values traversing a topology, thereby computing a global sum or minimum. Similarly, computations may concatenate or filter messages in order to implement program monitoring, I/O, file storage, or virtual terminal services.Topologies are implemented as an extension of the Intel iPSC hypercube's operating system kernel and have been used with several, large-scale parallel application programs.