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C plus plus Support and Applications for Embedded Multicore DSP Systems

JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY 2014年第2期75卷 109-122页

作者： Kuan, Chi-Bang Li, Jia-Jhe Chen, Chung-Kai Lee, Jenq-Kuen Natl Tsing Hua Univ Dept Comp Sci Hsinchu 30043 Taiwan Realtek Semicond Corp Hsinchu Taiwan

In recent years embedded systems have entered the multicore era. As the number of cores keeps growing in embedded systems, it becomes more important to provide programming support which considers embedded system constraints and in the meanwhile helps utilize multicore systems. So far though C still dominates embedded programming, C++ is gaining in importance in parallel programming. It is promising to support C++ for embedded multicore systems. However, embedded systems usually have tight resource budgets, and C++ is commonly considered having huge code size that embedded systems can not afford. Therefore, in this paper we investigate the code size requirement of a C++ library and propose a layered design to provide a code size aware library support. On the other hand, to utilize embedded multicore systems, we employ C++ linguistic features to facilitate embedded multicore programming. With C++, we incorporate high-level abstractions and design patterns into the programming support to enhance low-level programming APIs that can be used to exploit DSPs, SIMD instructions, and DMAs on embedded multicore systems. At last, we evaluate our C++ support with a Blur and a JPEG program. Our result on a dual-DSP platform shows that we can obtain speedups of 3.32 and 3.09 for the Blur and JPEG program, respectively.

关键词： Embedded multicore systems C plus plus library programming models Parallel design patterns

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Parallel programming Paradigms and Frameworks in Big Data Era

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INTERNATIONAL JOURNAL OF PARALLEL programming 2014年第5期42卷 710-738页

作者： Dobre, Ciprian Xhafa, Fatos Univ Politehn Bucuresti Dept Comp Sci Bucharest Romania Univ Politecn Cataluna ES-08034 Barcelona Spain

With Cloud Computing emerging as a promising new approach for ad-hoc parallel data processing, major companies have started to integrate frameworks for parallel data processing in their product portfolio, making it easy for customers to access these services and to deploy their programs. We have entered the Era of Big Data. The explosion and profusion of available data in a wide range of application domains rise up new challenges and opportunities in a plethora of disciplines-ranging from science and engineering to biology and business. One major challenge is how to take advantage of the unprecedented scale of data-typically of heterogeneous nature-in order to acquire further insights and knowledge for improving the quality of the offered services. To exploit this new resource, we need to scale up and scale out both our infrastructures and standard techniques. Our society is already data-rich, but the question remains whether or not we have the conceptual tools to handle it. In this paper we discuss and analyze opportunities and challenges for efficient parallel data processing. Big Data is the next frontier for innovation, competition, and productivity, and many solutions continue to appear, partly supported by the considerable enthusiasm around the MapReduce paradigm for large-scale data analysis. We review various parallel and distributed programming paradigms, analyzing how they fit into the Big Data era, and present modern emerging paradigms and frameworks. To better support practitioners interesting in this domain, we end with an analysis of on-going research challenges towards the truly fourth generation data-intensive science.

关键词： Parallel programming Big Data MapReduce programming models Challenges

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A Survey of Software Frameworks for Cluster-Based Large High-Resolution Displays

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2014年第8期20卷 1158-1177页

作者： Chung, Haeyong Andrews, Christopher North, Chris Virginia Polytech Inst & State Univ Dept Comp Sci Blacksburg VA 24060 USA Middlebury Coll Dept Comp Sci Middlebury VT 05753 USA

Large high-resolution displays (LHRD) enable visualization of extremely large-scale data sets with high resolution, large physical size, scalable rendering performance, advanced interaction methods, and collaboration. Despite the advantages, applications for LHRD can be developed only by a select group of researchers and programmers, since its software implementation requires design and development paradigms different from typical desktop environments. It is critical for developers to understand and take advantage of appropriate software tools and methods for developing their LHRD applications. In this paper, we present a survey of the state-of-the-art software frameworks and applications for cluster-based LHRD, highlighting a three-aspect taxonomy. This survey can aid LHRD application and framework developers in choosing more suitable development techniques and software environments for new LHRD applications, and guide LHRD researchers to open needs in LHRD software frameworks.

关键词： Large high-resolution display tiled displays distributed rendering parallel rendering distributed applications graphics api large scale visualization programming models input devices and strategies

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Edelweiss: Automatic Storage Reclamation for Distributed programming

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PROCEEDINGS OF THE VLDB ENDOWMENT 2014年第6期7卷 481-492页

作者： Conway, Neil Alvaro, Peter Andrews, Emily Hellerstein, Joseph M. Univ Calif Berkeley Berkeley CA 94720 USA

Event Log Exchange (ELE) is a common programming pattern based on immutable state and messaging. ELE sidesteps traditional challenges in distributed consistency, at the expense of introducing new challenges in designing space reclamation protocols to avoid consuming unbounded storage. We introduce Edelweiss, a sublanguage of Bloom that provides an ELE programming model, yet automatically reclaims space without programmer assistance. We describe techniques to analyze Edelweiss programs and automatically generate application-specific distributed space reclamation logic. We show how Edelweiss can be used to elegantly implement a variety of communication and distributed storage protocols;the storage reclamation code generated by Edelweiss effectively garbage-collects state and often matches hand-written protocols from the literature.

关键词： Reclamation Application programs Application specific Distributed programming Distributed storage programming models programming patterns

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Recent advances in the Message Passing Interface

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2014年第4期28卷 387-389页

作者： Garcia Blas, Javier Carretero, Jesus Univ Carlos III Madrid E-28903 Getafe Spain

An introduction is presented in which the editor discusses various reports within the issue on topics including Message Passing Interface (MPI), parallel input/output (I/O), and parallel programming.

关键词： Parallel I O parallel systems parallel programming benchmarking programming models

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OPERATING SYSTEMS RESEARCH FOR RECONFIGURABLE COMPUTING

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IEEE MICRO 2014年第1期34卷 55-59页

作者： Andrews, David Univ Arkansas Dept Comp Sci & Comp Engn Mullins Endowed Chair Comp Engn Fayetteville AR 72701 USA

THIS ARTICLE OVERVIEWS HOW THE RAPID CHANGES TO FIELD-PROGRAMMABLE GATE ARRAY (FPGA) DEVICES HAVE REFOCUSED OPERATING SYSTEMS RESEARCH WITHIN RECONFIGURABLE COMPUTING. WHERE THE GOAL WAS ONCE TO SIMPLY USE EXISTING OPERATING SYSTEMS, FPGA-BASED OS RESEARCH MAY NOW BE IN THE NEW POSITION OF HELPING DEFINE THE LOOK AND FEEL OF NEXT-GENERATION OPERATING SYSTEMS FOR EVOLVING CHIP-HETEROGENEOUS MULTIPROCESSOR SYSTEMS.

关键词： computer architecture Field programmable gate arrays FPGA heterogeneous multiprocessors multiprocessor systems on chip operating systems parallel and distributed systems Program processors programming models Real-time systems Reconfigurable architectures reconfigurable computing

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Discovery of biological networks using an optimized partial correlation coefficient with information theory algorithm on Stampede's Xeon and Xeon Phi processors

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2014年第13期26卷 2178-2190页

作者： Koesterke, Lars Koltes, James E. Weeks, Nathan T. Milfeld, Kent Vaughn, Matthew W. Reecy, James M. Stanzione, Dan Univ Texas Austin Texas Adv Comp Ctr Austin TX 78712 USA Iowa State Univ Dept Anim Sci High Performance Comp Grp Ames IA USA

The partial correlation coefficient with information theory (PCIT) method is an important technique for detecting interactions between networks. The PCIT algorithm has been used in the biological context to infer complex regulatory mechanisms and interactions in genetic networks, in genome wide association studies, and in other similar problems. In this work, the PCIT algorithm is re-implemented with exemplary parallel, vector, input/output (I/O), memory, and instruction optimizations for today's multi-core and many-core architectures. The evolution and performance of the new code targets the processor architectures of the Stampede supercomputer but will also benefit other architectures. The Stampede system consists of an Intel Xeon E5 processor base system with an innovative component consist of Intel Xeon Phi Coprocessors. Optimized results and an analysis are presented for both the Xeon and the Xeon Phi. Copyright (c) 2014 John Wiley & Sons, Ltd.

关键词： accelerated computing programming models information theory algorithms performance design experimentation human factors languages

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Optimizing Collective Communication in UPC 28

Optimizing Collective Communication in UPC

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28th IEEE International Parallel & Distributed Processing Symposium Workshops (IPDPSW)

作者： Jose, Jithin Hamidouche, Khaled Zhang, Jie Venkatesh, Akshay Panda, Dhabaleswar K. (DK) Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA

ISBN: (纸本)9781479941162

Message Passing Interface (MPI) has been the defacto programming model for scientific parallel applications. However, data driven applications with irregular communication patterns are harder to implement using MPI. The Partitioned Global Address Space (PGAS) programming models present an alternative approach to improve programmability. PGAS languages like UPC are growing in popularity because of their ability to provide shared-memory programming model over distributed memory machines. However, since UPC is an emerging standard, it is unlikely that entire applications will be re-written with it. Instead, unified communication runtimes have paved the way for a new class of hybrid applications that can leverage the benefits of both MPI and PGAS models. Such unified runtimes need to be designed in a high performance, scalable manner to improve the performance of emerging hybrid applications. Collective communication primitives offer a flexible, portable way to implement group communication operations and are supported in both MPI and PGAS programming models. Owing to their advantages, they are also widely used across various scientific parallel applications. Over the years, MPI libraries have relied upon aggressive software-/hardware-based and kernel-assisted optimizations to deliver low communication latency for various collective operations. However, there is much room for improvement for collective operations in state-of-the-art, open-source implementations of UPC. In this paper, we address the challenges associated with improving the performance of collective primitives in UPC. Further, we also explore design alternatives to enable collective primitives in UPC to directly leverage the designs available in the MVAPICH2 MPI library. Our experimental evaluations show that our designs improve the performance of the UPC broadcast and all-gather operations, by 25X and 18X respectively for 128KB message at 2,048 processes. Our designs improve the performance of the UPC 2D

关键词： UPC Collectives InfiniBand programming models PGAS

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Compiler Techniques for Massively Scalable Implicit Task Parallelism 14

Compiler Techniques for Massively Scalable Implicit Task Par...

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International Conference on High Performance Computing, Networking, Storage and Analysis

作者： Armstrong, Timothy G. Wozniak, Justin M. Wilde, Michael Foster, Ian T. Univ Chicago Dept Comp Sci Chicago IL 60637 USA Argonne Natl Lab Math & Comp Sci Div Argonne IL 60439 USA Univ Chicago Computat Inst Chicago IL 60637 USA Argonne Natl Lab Chicago IL USA

ISBN: (纸本)9781479955008

Swift/T is a high-level language for writing concise, deterministic scripts that compose serial or parallel codes implemented in lower-level programming models into large-scale parallel applications. It executes using a data-driven task parallel execution model that is capable of orchestrating millions of concurrently executing asynchronous tasks on homogeneous or heterogeneous resources. Producing code that executes efficiently at this scale requires sophisticated compiler transformations: poorly optimized code inhibits scaling with excessive synchronization and communication. We present a comprehensive set of compiler techniques for data-driven task parallelism, including novel compiler optimizations and intermediate representations. We report application benchmark studies, including unbalanced tree search and simulated annealing, and demonstrate that our techniques greatly reduce communication overhead and enable extreme scalability, distributing up to 612 million dynamically load balanced tasks per second at scales of up to 262,144 cores without explicit parallelism, synchronization, or load balancing in application code.

关键词： Parallel scripting Swift High-performance computing Compiler optimization Data-flow Execution models programming models

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A runtime approach to dynamic resource allocation for sparse direct solvers 43

A runtime approach to dynamic resource allocation for sparse...

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43rd Annual International Conference on Parallel Processing (ICPP)

作者： Hugo, A. -E Guermouche, A. Wacrenier, P. -A. Namyst, R. Univ Bordeaux INRIA LaBRI Talence France

ISBN: (纸本)9781479956180

To face the advent of multicore processors and the ever increasing complexity of hardware architectures, programming models based on DAG-of-tasks parallelism regained popularity in the high performance, scientific computing community. In this context, enabling HPC applications to perform efficiently when dealing with graphs of parallel tasks that could potentially run simultaneously is a great challenge. Even if a uniform runtime system is used underneath, scheduling multiple parallel tasks over the same set of hardware resources introduces many issues, such as undesirable cache flushes or memory bus contention. In this paper, we show how runtime system-based scheduling contexts can be used to dynamically enforce locality of parallel tasks on multicore machines. We extend an existing generic sparse direct solver to use our mechanism and introduce a new decomposition method based on proportional mapping that is used to build the scheduling contexts. We propose a runtime-level dynamic context management policy to cope with the very irregular behavior of the application. A detailed performance analysis shows significant performance improvements of the solver over various multicore hardware.

关键词： cache storage directed graphs multiprocessing systems parallel processing processor scheduling resource allocation DAG-of-tasks parallelism decomposition method dynamic resource allocation generic sparse direct solver hardware architecture hardware resources irregular behaviour memory bus contention multicore hardware multicore machine multicore processor performance analysis programming models proportional mapping runtime approach runtime system-based scheduling context runtime-level dynamic context management policy scheduling multiple parallel task scientific computing community sparse direct solvers undesirable cache flush Context Dynamic scheduling Processor scheduling Program processors Runtime Sparse matrices Virtual machine monitors Composability Ressource allocation Sparse direct solver

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