Download or read book Optimizing for Parallelism and Data Locality written by Rice University. Dept. of Computer Science and published by . This book was released on 1992 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "Previous research has used program transformation to introduce parallelism and to exploit data locality. Unfortunately, these two objectives have usually been considered independently. This work explores the trade-offs between effectively utilizing parallelism and memory hierarchy on shared-memory multiprocessors. We present a simple, but suprisingly accurate, memory model to determine cache line reuse from both multiple accesses to the same memory location and from consecutive memory access. The model is used in memory optimizing and loop parallelization algorithms that effectively exploit data locality and parallelism in concert. We demonstrate the efficacy of this approach with very encouraging experimental results."

Download or read book Languages and Compilers for Parallel Computing written by Utpal Banerjee and published by Springer Science & Business Media. This book was released on 1994-01-28 with total page 678 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains papers selected for presentation at the Sixth Annual Workshop on Languages and Compilers for Parallel Computing. The workshop washosted by the Oregon Graduate Institute of Science and Technology. All the major research efforts in parallel languages and compilers are represented in this workshop series. The 36 papers in the volume aregrouped under nine headings: dynamic data structures, parallel languages, High Performance Fortran, loop transformation, logic and dataflow language implementations, fine grain parallelism, scalar analysis, parallelizing compilers, and analysis of parallel programs. The book represents a valuable snapshot of the state of research in the field in 1993.

Download or read book Optimizing Locality and Parallelism Through Program Reorganization written by Sriram Krishnamoorthy and published by . This book was released on 2008 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Development of scalable application codes requires an understanding and exploitation of the locality and parallelism in the computation. This is typically achieved through optimizations by the programmer to match the application characteristics to the architectural features exposed by the parallel programming model. Partitioned address space programming models such as MPI foist a process-centric view of the parallel system, increasing the complexity of parallel programming. Typical global address space models provide a shared memory view that greatly simplifies programming. But the simplified models abstract away the locality information, precluding optimized implementations. In this work, we present techniques to reorganize program execution to optimize locality and parallelism, with little effort from the programmer. For regular loop-based programs operating on dense multi-dimensional arrays, we propose an automatic parallelization technique that attempts to determine a parallel schedule in which all processes can start execution in parallel. When the concurrent tiled iteration space inhibits such execution, we present techniques to re-enable it. This is an alternative to incurring the pipelined startup overhead in schedules generated by prevalent approaches. For less structured programs, we propose a programming model that exposes multiple levels abstraction to the programmer. These abstractions enable quick prototyping coupled with incremental optimizations. The data abstraction provides a global view of distributed data organized as blocks. A block is a subset of data stored contiguously in a single process' address space. The computation is specified as a collection of tasks operating on the data blocks, with parallelism and dependence being specified between them. When the blocking of the data does not match the required access pattern in the computation, the data needs to be reblocked to improve spatial locality. We develop efficient data layout transformation mechanisms for blocked multi-dimensional arrays. We also present mechanisms for automatic management of load balance, disk I/O, and inter-process communication on computations expressed as sets of independent tasks on blocked data stored on disk.

Download or read book Optimizing Data Locality and Parallelism for Scalable Multiprocessors written by Ashish Narayan and published by . This book was released on 1994 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Improving Parallelism and Data Locality with Affine Partitioning written by Amy Wingmui Lim and published by . This book was released on 2001 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Improving Locality and Parallelism in Nested Loops written by Michael Edward Wolf and published by . This book was released on 1992 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Euro Par 2010 Parallel Processing written by Pasqua D'Ambra and published by Springer Science & Business Media. This book was released on 2010-08-18 with total page 626 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 16th International Euro-Par Conference held in Ischia, Italy, in August/September 2010. The 90 revised full papers presented were carefully reviewed and selected from 256 submissions. The papers are organized in topical sections on support tools and environments; performance prediction and evaluation; scheduling and load-balancing; high performance architectures and compilers; parallel and distributed data management; grid, cluster and cloud computing; peer to peer computing; distributed systems and algorithms; parallel and distributed programming; parallel numerical algorithms; multicore and manycore programming; theory and algorithms for parallel computation; high performance networks; and mobile and ubiquitous computing.

Download or read book Optimizing Parallel Programs Using Composable Locality Models written by Hao Luo and published by . This book was released on 2017 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: "On modern processors, the on-chip cache memory is structured in a hierarchy, in order to accommodate the rapidly growing disparity between processor peak speed and off-chip memory speed. This design makes a program's performance highly correlated with its memory access pattern and where the accessed data are positioned within the hierarchy. Locality analysis is to study such correlation and optimize programs accordingly. However, the existing research effort in locality analysis is rather limited when dealing with contemporary parallel workloads. The performance of these workloads can be significantly influenced by how their threads interactively access data. The state of the art in locality analysis is neither sufficient nor efficient in modeling such interaction. Therefore, in this dissertation, I will present a set of locality models to analyze modern parallel workloads. The new models give insights on how the threads share data on a quantitative basis. They have a common property, composability, which makes predicting cache miss ratio extremely efficient, especially for a large number of thread and data placements. I will also show how these models enable new optimizations that significantly improve the performance of GPU applications and parallel workloads on NUMA systems."--Page x.

Download or read book F for Scientists written by Jon Harrop and published by John Wiley & Sons. This book was released on 2011-09-20 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This work strikes a balance between the pure functional aspects of F# and the object-oriented and imperative features that make it so useful in practice, enable .NET integration, and make large-scale data processing possible." —Thore Graepel, PhD, Researcher, Microsoft Research Ltd. Over the next five years, F# is expected to become one of the world's most popular functional programming languages for scientists of all disciplines working on the Windows platform. F# is free and, unlike MATLAB® and other software with numerical/scientific origins, is a full-fledged programming language. Developed in consultation with Don Syme of Microsoft Research Ltd.—who wrote the language—F# for Scientists explains and demonstrates the powerful features of this important new programming language. The book assumes no prior experience and guides the reader from the basics of computer programming to the implementation of state-of-the-art algorithms. F# for Scientists begins with coverage of introductory material in the areas of functional programming, .NET, and scientific computing, and goes on to explore: Program structure Optimization Data structures Libraries Numerical analysis Databases Input and output Interoperability Visualization Screenshots of development using Visual Studio are used to illustrate compilation, debugging, and interactive use, while complete examples of a few whole programs are included to give readers a complete view of F#'s capabilities. Written in a clear and concise style, F# for Scientists is well suited for researchers, scientists, and developers who want to program under the Windows platform. It also serves as an ideal supplemental text for advanced undergraduate and graduate students with a background in science or engineering.

Download or read book Languages and Compilers for Parallel Computing written by Keshav Pingali and published by Springer Science & Business Media. This book was released on 1995-01-26 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents revised versions of the 32 papers accepted for the Seventh Annual Workshop on Languages and Compilers for Parallel Computing, held in Ithaca, NY in August 1994. The 32 papers presented report on the leading research activities in languages and compilers for parallel computing and thus reflect the state of the art in the field. The volume is organized in sections on fine-grain parallelism, align- ment and distribution, postlinear loop transformation, parallel structures, program analysis, computer communication, automatic parallelization, languages for parallelism, scheduling and program optimization, and program evaluation.

Download or read book OpenMP Shared Memory Parallel Programming written by Matthias S. Müller and published by Springer. This book was released on 2008-05-23 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-workshop proceedings of the First and the Second International Workshop on OpenMP, IWOMP 2005 and IWOMP 2006, held in Eugene, OR, USA, and in Reims, France, in June 2005 and 2006 respectively. The first part of the book presents 16 revised full papers carefully reviewed and selected from the IWOMP 2005 program and organized in topical sections on performance tools, compiler technology, run-time environment, applications, as well as the OpenMP language and its evaluation. In the second part there are 19 papers of IWOMP 2006, fully revised and grouped thematically in sections on advanced performance tuning aspects of code development applications, and proposed extensions to OpenMP.

Download or read book A Fresh Look At Data Locality On Emerging Multicores And Manycores written by Wei Ding and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The emergence of multicore platforms offers several opportunities for boosting ap- plication performance. These opportunities, which include parallelism and data locality benefits, require strong support from compilers as well as operating sys- tems. However, architectural abstractions relevant to memory system are scarce in current programming and compiler systems. In fact, most compilers do not take any memory system specific parameter into account even when they are perform- ing data locality optimizations. Instead, their locality optimizations are driven by rule-of-thumbs such as "maximizing stride-1 accesses in innermost loop positions". There are a few compilers that take cache and memory specific parameters into account to look at the data locality problem in a global sense.One of these parameters is the on-chip cache hierarchy, which determines the core connection and thus data sharing between computations on different cores. Another parameter is the memory controller. In a network-on-chip (NoC) based multicore architecture, an off-chip data access (main memory access) needs to travel through the on-chip network, spending considerable amount of time within the chip (in addition to the memory access latency). In addition, it contends with on-chip (cache) accesses as both use the same NoC resources. The third parameter that will be discussed in this thesis is the row-buffer. Many emerging multicores employ banked memory systems and each bank is attached a row-buffer that holds the most-recently accessed memory row (page). A last-level cache miss that also misses in the row-buffer can experience much higher latency than a cache miss that hits in the row-buffer. Consequently, optimizing for row-buffer locality can be as important as optimizing for cache locality.Motivated by this, in this thesis, we propose four different compiler-directed " locality" optimization schemes that take these parameters into account. Specifi- cally, our first scheme targets cache hierarchy-aware loop transformation strategy for multicore architectures. It determines a loop iteration-to-core mapping byiiitaking into account application data access pattern and multicore on-chip cache hierarchy. It employs "core vectors" to exploit data reuses at different layers of cache hierarchy based on their reuse distances, with the goal of maximizing data lo- cality at each level while minimizing the data dependences across the cores. In case of dependence free loop nest, we customize our loop scheduling strategy, which, on the other hand, determines a schedule for the iterations assigned to each core, with the goal of reducing data reuse distances across the cores. Our experimental evaluation shows that the proposed loop transformation scheme reduces miss rates at all levels of caches and application execution time significantly, and when sup- ported by scheduling, the reduction in cache miss rates and execution time become much larger.The second scheme explores automatic data layout transformation targeting multithreaded applications running on multicores (which is also cache hierarchy- aware). Our transformation considers both data access patterns exhibited by dif- ferent threads of a multithreaded application and the on-chip cache topology of the target multicore architecture. It automatically determines a customized memory layout for each target array to minimize potential cache conflicts across threads. Our experiments show that, our optimization brings significant benefits over state- of-the-art data locality optimization strategies when tested using 22 benchmark programs on an Intel multicore machine. The results also indicate that this strat- egy is able to scale to larger core counts and it performs better with increased data set sizes.In the third scheme, focusing on multithreaded applications, we propose a compiler-guided off-chip data access localization strategy, which places data el- ements in the memory space such that an off-chip access traverses a minimum number of links (hops) to reach the controller that handles this access request. We present an extensive experimental evaluation of our compiler-guided optimization strategy using a set of 12 multithreaded application programs under both private and shared last level caches. The results collected emphasize the importance of optimizing the off-chip data accesses.The fourth scheme presents a compiler-directed row-buffer locality optimization strategy. This strategy modifies the memory layout of data to increase the number of row-buffer hits without increasing the number of misses in the on-chip cache hierarchy. We implemented our proposed optimization strategy in an open-source compiler and tested its effectiveness in improving the row-buffer performance using a set of multithreaded applications.

Download or read book Parallel Optimization written by Yair Censor and published by Oxford University Press, USA. This book was released on 1997 with total page 574 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a unique pathway to methods of parallel optimization by introducing parallel computing ideas into both optimization theory and into some numerical algorithms for large-scale optimization problems. The three parts of the book bring together relevant theory, careful study of algorithms, and modeling of significant real world problems such as image reconstruction, radiation therapy treatment planning, financial planning, transportation and multi-commodity network flow problems, planning under uncertainty, and matrix balancing problems.

Download or read book Proceedings of the 1993 International Conference on Parallel Processing written by Alok N. Choudhary and published by CRC Press. This book was released on 1993-08-16 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three-volume work presents a compendium of current and seminal papers on parallel/distributed processing offered at the 22nd International Conference on Parallel Processing, held August 16-20, 1993 in Chicago, Illinois. Topics include processor architectures; mapping algorithms to parallel systems, performance evaluations; fault diagnosis, recovery, and tolerance; cube networks; portable software; synchronization; compilers; hypercube computing; and image processing and graphics. Computer professionals in parallel processing, distributed systems, and software engineering will find this book essential to their complete computer reference library.

Download or read book Optimizing Parallel Job Performance in Data Intensive Clusters written by Ganesh Ananthanarayanan and published by . This book was released on 2014 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: Extensive data analysis has become the enabler for diagnostics and decision making in many modern systems. These analyses have both competitive as well as social benefits. To cope with the deluge in data that is growing faster than Moore's law, computation frameworks have resorted to massive parallelization of analytics jobs into many fine-grained tasks. These frameworks promised to provide efficient and fault-tolerant execution of these tasks. However, meeting this promise in clusters spanning hundreds of thousands of machines is challenging and a key departure from earlier work on parallel computing. A simple but key aspect of parallel jobs is the all-or-nothing property: unless all tasks of a job are provided equal improvement, there is no speedup in the completion of the job. The all-or-nothing property is critical for the promise of efficient and fault-tolerant parallel computations on large clusters. Meeting this promise in clusters of these scales is challenging and a key departure from prior work on distributed systems. This work examines the execution of a job from first principles and propose techniques spanning the software stack of data analytics systems such that its tasks achieve homogeneous performance while overcoming the various heterogeneities. To that end, we will propose techniques for (i) caching and cache replacement for parallel jobs, which outperforms even Belady's MIN (that uses an oracle), (ii) data locality, and (iii) straggler mitigation. Our analyses and evaluation are performed using workloads from Facebook and Bing production datacenters. Along the way, we will also describe how we broke the myth of disk-locality's importance in datacenter computing.

Download or read book Computational Science ICCS 2003 written by Peter M.A. Sloot and published by Springer. This book was released on 2003-08-03 with total page 1183 pages. Available in PDF, EPUB and Kindle. Book excerpt: Some of the most challenging problems in science and engineering are being addressed by the integration of computation and science, a research ?eld known as computational science. Computational science plays a vital role in fundamental advances in biology, physics, chemistry, astronomy, and a host of other disciplines. This is through the coordination of computation, data management, access to instrumentation, knowledge synthesis, and the use of new devices. It has an impact on researchers and practitioners in the sciences and beyond. The sheer size of many challenges in computational science dictates the use of supercomputing, parallel and distri- ted processing, grid-based processing, advanced visualization and sophisticated algorithms. At the dawn of the 21st century the series of International Conferences on Computational Science (ICCS) was initiated with a ?rst meeting in May 2001 in San Francisco. The success of that meeting motivated the organization of the - cond meeting held in Amsterdam April 21–24, 2002, where over 500 participants pushed the research ?eld further. The International Conference on Computational Science 2003 (ICCS 2003) is the follow-up to these earlier conferences. ICCS 2003 is unique, in that it was a single event held at two di?erent sites almost opposite each other on the globe – Melbourne, Australia and St. Petersburg, Russian Federation. The conference ran on the same dates at both locations and all the presented work was published in a single set of proceedings, which you hold in your hands right now.

Download or read book Languages and Compilers for Parallel Computing written by Xipeng Shen and published by Springer. This book was released on 2016-02-19 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the 28th International Workshop on Languages and Compilers for Parallel Computing, LCPC 2015, held in Raleigh, NC, USA, in September 2015. The 19 revised full papers were carefully reviewed and selected from 44 submissions. The papers are organized in topical sections on programming models, optimizing framework, parallelizing compiler, communication and locality, parallel applications and data structures, and correctness and reliability.