Part 1 Introduction: heterogeneous network computing trends in distributed computing PVM overview other packages. Part 2 The PVM system. Part 3 Using PVM: how to obtain the PVM software setup to use PVM setup summary starting PVM common startup problems running PVM programs PVM console details host file options. Part 4 Basic programming techniques: common parallel programming paradigms workload allocation porting existing applications to PVM. Part 5 PVM user interface: process control information dynamic configuration signalling setting and getting options message passing dynamic process groups. Part 6 Program examples: fork-join dot product failure matrix multiply one-dimensional heat equation. Part 7 How PVM works: components messages PVM daemon libpvm library protocols message routing task environment console program resource limitations multiprocessor systems. Part 8 Advanced topics: XPVM porting PVM to new architectures. Part 9 Troubleshooting: geting PVM installed getting PVM running compiling applications running applications debugging and tracing debugging the system. Appendices: history of PVM versions PVM 3 routines.

PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing

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[서평]「Computer Organization and Design, The Hardware/Software Interface」

The underutilization of disk parallelism and file cache buffers by traditional file systems induces I/O stall time that degrades the performance of modern microprocessor-based systems. In this paper, we present aggressive mechanisms that tailor file system resource management to the needs of I/O-intensive applications. In particular, we show how to use application-disclosed access patterns (hints) to expose and exploit I/O parallelism and to allocate dynamically file buffers among three competing demands: prefetching hinted blocks, caching hinted blocks for reuse, and caching recently used data for unhinted accesses. Our approach estimates the impact of alternative buffer allocations on application execution time and applies a cost-benefit analysis to allocate buffers where they will have the greatest impact. We implemented informed prefetching and caching in DEC''s OSF/1 operating system and measured its performance on a 150 MHz Alpha equipped with 15 disks running a range of applications including text search, 3D scientific visualization, relational database queries, speech recognition, and computational chemistry. Informed prefetching reduces the execution time of the first four of these applications by 20% to 87%. Informed caching reduces the execution time of the fifth application by up to 30%.

/pdf/informed-prefetching-and-caching-4nm5o41nvm.pdf

Informed prefetching and caching

From the Publisher:
The Message Passing Interface (MPI) specification is widely used for solving significant scientific and engineering problems on parallel computers There exist more than a dozen implementations on computer platforms ranging from IBM SP-2 supercomputers to clusters of PCs running Windows NT or Linux ("Beowulf" machines) The initial MPI Standard document, MPI-1, was recently updated by the MPI Forum The new version, MPI-2, contains both significant enhancements to the existing MPI core and new features
Using MPI is a completely up-to-date version of the authors' 1994 introduction to the core functions of MPI It adds material on the new C++ and Fortran 90 bindings for MPI throughout the book It contains greater discussion of datatype extents, the most frequently misunderstood feature of MPI-1, as well as material on the new extensions to basic MPI functionality added by the MPI-2 Forum in the area of MPI datatypes and collective operations
Using MPI-2 covers the new extensions to basic MPI These include parallel I/O, remote memory access operations, and dynamic process management The volume also includes material on tuning MPI applications for high performance on modern MPI implementations

Using MPI-2: Advanced Features of the Message Passing Interface

The Sonification Handbook

Network, Web, and disk I/O traffic are usually bursty and self-similar and therefore cannot be modeled adequately with Poisson arrivals. However, we wish to model these types of traffic and generate realistic traces, because of obvious applications for disk scheduling, network management, and Web server design. Previous models (like fractional Brownian motion and FARIMA, etc.) tried to capture the 'burstiness'. However, the proposed models either require too many parameters to fit and/or require prohibitively large (quadratic) time to generate large traces. We propose a simple, parsimonious method, the b-model, which solves both problems: it requires just one parameter, and can easily generate large traces. In addition, it has many more attractive properties: (a) with our proposed estimation algorithm, it requires just a single pass over the actual trace to estimate b. For example, a one-day-long disk trace in milliseconds contains about 86 Mb data points and requires about 3 minutes for model fitting and 5 minutes for generation. (b) The resulting synthetic traces are very realistic: our experiments on real disk and Web traces show that our synthetic traces match the real ones very well in terms of queuing behavior.

/pdf/data-mining-meets-performance-evaluation-fast-algorithms-for-htg55bp9ak.pdf

Data mining meets performance evaluation: fast algorithms for modeling bursty traffic

As parallel systems expand in size and complexity, the absence of performance tools for these parallel systems exacerbates the already difficult problems of application program and system software performance tuning. Moreover, given the pace of technological change, we can no longer afford to develop ad hoc, one-of-a-kind performance instrumentation software; we need scalable, portable performance analysis tools. We describe an environment prototype based on the lessons learned from two previous generations of performance data analysis software. Our environment prototype contains a set of performance data transformation modules that can be interconnected in user-specified ways. It is the responsibility of the environment infrastructure to hide details of module interconnection and data sharing. The environment is written in C++ with the graphical displays based on X windows and the Motif toolkit. It allows users to interconnect and configure modules graphically to form an acyclic, directed data analysis graph. Performance trace data are represented in a self-documenting stream format that includes internal definitions of data types, sizes, and names. The environment prototype supports the use of head-mounted displays and sonic data presentation in addition to the traditional use of visual techniques.

Scalable Performance Environments for Parallel Systems

Input/output performance on current parallel file systems is sensitive to a good match of application access pattern to file system capabilities. Automaticiuput/output access classification can determine application access patterns at execution time, guiding adaptive file system policies. In this paper we examine a new method for access pattern classification that uses hidden Markov models, trained on access patterns from previous executions, to create a probabilistic model of input/output accesses. We compare this approach to a neural network classification &n-rework, presenting performance results from parallel and sequential benchmarks and applications.

Input/output access pattern classification using hidden Markov models

The authors assert that, despite great strides in developing the graphical dimension of user interfaces, the auditory dimension has been neglected. They offer a tool for using sound to complement visual cues when working with complex data. The Porsonify toolkit creates sonifications that can be easily integrated with existing visualization systems. Porsonify provides a platform to experimentally map data to sound. To provide portability, Porsonify relies on a network interface to sound devices and descriptions of abstract sound devices. By manipulating abstract sound devices, Porsonify allows access to unique, device-specific features, while hiding their complexities within the higher level concept of a sonification. This structure makes it easy to create sonifications, both in isolation and with accompanying visualizations. >

Data sonification: do you see what I hear?

Input/output performance on current parallel file systems is sensitive to a good match of application access patterns to file system capabilities. Automatic input/output access pattern classification can determine application access patterns at execution time, guiding adaptive file system policies. In this paper, we examine and compare two novel input/output access pattern classification methods based on learning algorithms. The first approach uses a feedforward neural network previously trained on access pattern benchmarks to generate qualitative classifications. The second approach uses hidden Markov models trained on access patterns from previous executions to create a probabilistic model of input/output accesses. In a parallel application, access patterns can be recognized at the level of each local thread or as the global interleaving of all application threads. Classification of patterns at both levels is important for parallel file system performance; we propose a method for forming global classifications from local classifications. We present results from parallel and sequential benchmarks and applications that demonstrate the viability of this approach.

Tara M. Madhyastha

Papers

Data mining meets performance evaluation: fast algorithms for modeling bursty traffic

Scalable Performance Environments for Parallel Systems

Input/output access pattern classification using hidden Markov models

Data sonification: do you see what I hear?

Learning to classify parallel input/output access patterns