SIMD

In this work we present a programmable and reconfigurable
single instruction multiple data (SIMD) visual processor
based on the S-CNN architecture, namely, the Simplicial CNN Digital Visual Processor (SCDVP), oriented to high-performance low level image processing. The cells in the array have a selectable
neighborhood configuration and several registers, which provide
the chip with extended spatial and temporal processing capabilities, in particular optical flow. A prototype 64 64 cell chip with two program memories and a column adder was fabricated in a 90 nm technology, which running at 133 MHz delivers 105.5 GOPS.
The calculation at the cell level is performed with time coded signals and the program memory is located outside the array. This
produces a very efficient realization in terms of area: 53.8 GOPS
per mm , which outperforms all results reported so far. We show
that even after normalization, to account for technology scaling,
the proposed architecture is the most efficient among all reported
digital processors. Computation performance to power ratio also
exceeds all previous results with 817.8 GOPS/W. Experimental results of the working chip are reported.

Molecular dynamics (MD) simulations are able to provide a wealth of detailed information about biological systems enabling studies impossible to perform in a laboratory. As such, MD simulations provide insight into important biological processes like protein folding, the mechanism of viral infections, as well as aid modern drug design.

Typical MD simulations require billions of time-steps, at each step evaluating forces between tens to hundreds of thousands of particles. This represents a floating-point bottleneck and suggests that the problem is well-suited for HPC. However, the iterative nature of MD algorithms means that speeding up simulations requires reducing the calculation time of a time-step. As the amount of parallelism is limited by the range of relevant problem sizes, improving absolute performance requires strong scaling. Highly optimized MD codes like GROMACS reach iteration rates in the millisecond range which renders MD strongly latency sensitive and represents unique challenges for efficient parallelization.

The HPC landscape is rapidly changing toward increasing on-chip parallelism as well as heterogeneity through emerging accelerator platforms like GPUs or Intel MIC.
The GROMACS MD package is known for its high performance thanks to hand-tuned SIMD kernels and state-of-the art parallel algorithms. However, making efficient use of modern architectures required a substantial redesign of the previously MPI-only parallelization as well as
entirely new algorithms for wide SIMD units and massive accelerator parallelism.

Here we present our recent work on multi-level heterogeneous parallelization of MD implemented in GROMACS. We have developed new, highly efficient SIMD algorithms for pair force calculation targeting current and future architectures ranging from CPUs to GPUs to FPGAs. Thanks to recent efforts, all compute-intensive code in GROMACS uses SIMD acceleration to maximize single-core CPU performance. OpenMP multithreading allows combining a set of cores with GPUs for node-level heterogeneous parallelization. On the top level, MPI-based neutral-territory domain-decomposition ensures scaling across compute nodes. To maximize the utilization of all compute resources, we employ multi-level, inter- and intra-node dynamic load balancing. The latest version of GROMACS not only provides a dramatic improvement in strong scaling to tens of particles per core at peak, but is able to efficiently utilize all compute units in heterogeneous HPC hardware with an absolute simulation performance between hundreds of nanoseconds to a microsecond a day.

A review of previous array Pascals leads on to a description the
Glasgow Pascal compiler. The compiler is an ISO-Pascal superset
with semantic extensions to translate data parallel statements to
run on multiple SIMD cores. An appendix is given which includes
demonstrations of the tool.

We live in a data centric world with the availability of information on web to satisfy the two folded objectives of secured easy access and quick processing. The web solutions, which connect users to information, are well equipped to provide the best facility to its users and web browsers as a platform, are used to deliver services offered by the large and complex applications. JavaScript (JS) is the most popular and pervasive web scripting language because it is supported by all the web browsers. But, new alternatives are always evaluated to overcome its shortcomings. With the advancement in technology, programming languages have also added the new computing strategies. One such introduced by Google is Dart. With the latest set of programming features, it is such a programming language which is designed to utilize direct hardware capabilities just like C/C++ along with maximum utilization of CPU. The idea discussed in this paper is that the use of Dart as a Programming Language wil...

Ada rasa dari masa lalu yang kadang menyeruak di waktu-waktu yang kini kita lewati. Sebagian menyenangkan, sebagian tidak. Apa lagi jika berhubungan dengan kisah dua anak manusia yang kandas di tengah jalan. Ada yang dapat melupakannya, sementara yang lain menyisakannya sebagai sebuah rasa yang tertinggal dari sebuah kenangan.
Semoga kita dapat mengambil pelajaran dari kisah-kisah ini. Selamat membaca, semoga Anda menemukan apa yang dicari disini.

Jakarta, Oktober 2020

"This Contemporary computer systems are multiprocessor or multicomputer machines. Their efficiency depends on good methods of administering the executed works. Fast processing of a parallel application is possible only when its parts are appropriately ordered in time and space. This calls for efficient scheduling policies in parallel computer systems. In this work deterministic problems of scheduling are considered. The classical scheduling theory assumed that the application in any moment of time is executed by only one processor. This assumption has been weakened recently, especially in the context of parallel and distributed computer systems. This monograph is devoted to problems of deterministic scheduling applications (or tasks according to the scheduling terminology) requiring more than one processor simultaneously. We name such applications multiprocessor tasks. In this work the complexity of open multiprocessor task scheduling problems has been established. Algorithms for scheduling multiprocessor tasks on parallel and dedicated processors are proposed. For a special case of applications with regular structure which allow for dividing it into parts of arbitrary size processed independently in parallel, a method of finding optimal scattering of work in a distributed computer system is proposed. The applications with such regular characteristics are called divisible tasks. The concept of a divisible task enables creation of tractable computation models in a wide class of computer architectures such as chains, stars, meshes, hypercubes, multistage networks. Divisible task method gives rise to the evaluation of computer system performance. Examples of such performance evaluation are presented. This work summarizes earlier works of the author as well as contains new original results. Mukul Varshney | Jyotsna | Abhakiran Rajpoot | Shivani Garg""Problems in Task Scheduling in Multiprocessor System"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2198.pdf
Article URL: http://www.ijtsrd.com/computer-science/computer-architecture/2198/problems-in-task-scheduling-in-multiprocessor-system/mukul-varshney"

We present an efficient O( n ) numerical algorithm for first-order approximation of geodesic distances on geometry images, where n is the number of points on the surface. The structure of our algorithm allows efficient implementation on parallel architectures. Two implementations on a SIMD processor and on a GPU are discussed. Numerical results demonstrate up to four orders of magnitude improvement in execution time compared to the state-of-the-art algorithms.

Fog and mobile edge computing is a paradigm that augments resource-scarce mobile devices with resource-rich network servers to enable ubiquitous computing. Smartphone applications rely on code offloading techniques to leverage high-performance computing opportunities available on edge and cloud servers for compute-intensive applications. Mobile (ARM) and edge/cloud (x86) architectures are heterogeneous and necessitate dynamic binary translation for compiled code migration that increases the application execution time. The application execution time and energy consumption should be lesser on the edge/cloud server as compared with the local mobile execution for optimal offload. Multimedia-based applications contain a large set of single instruction multiple data (SIMD) instructions that are compute and resource intensive. However, dynamic binary translation techniques of SIMD instructions lose the parallelism and optimization because of inefficient vector-to-scalar translation. We present a framework for SIMD instruction translation and offloading for mobile devices (SIMDOM) in cloud and edge environments. The SIMDOM framework reduces the execution overhead of migrated vectorized multimedia application by using vector-to-vector instruction mappings. The framework maps and translates ARM SIMD intrinsic instructions to x86 SIMD intrinsic instructions such that an application programmed for the mobile platform can be executed on the cloud server without any modification. The offload decision is based on inputs from the device energy, network, and application profilers. Experiments show that SIMDOM framework provides 84.78%, 3.41%, and 79.93% energy, time, and performance efficiency, respectively, compared with local offload-disabled execution. Compared with compiled code offloading, the SIMDOM framework provides 55.99%, 57.50%, and 96.23% energy, time, and performance efficiency, respectively.

The rapid increase in the performance of graphics hardware, coupled with recent improvements in its programmability, have made graphics hardware a compelling platform for computationally demanding tasks in a wide variety of application domains. In this report, we describe, summarize, and analyze the latest research in mapping general-purpose computation to graphics hardware.We begin with the technical motivations that underlie general-purpose computation on graphics processors (GPGPU) and describe the hardware and software developments that have led to the recent interest in this field. We then aim the main body of this report at two separate audiences. First, we describe the techniques used in mapping general-purpose computation to graphics hardware. We believe these techniques will be generally useful for researchers who plan to develop the next generation of GPGPU algorithms and techniques. Second, we survey and categorize the latest developments in general-purpose application development on graphics hardware.

Despite the widespread adoption of parallel operations in contemporary CPU designs, their use has been restricted by a lack of appropriate programming language abstractions and development environments. To fully exploit the SIMD model of computation such operations offer, programmers depend on CPU specific machine code or implementation-dependent libraries. Here we present vector Pascal (VP), a language designed to enable the elegant and efficient expression of SIMD algorithms. VP imports into Pascal abstraction ...

Performance of shared memory processors show negative performance impulses (drawbacks) in certain regions for execution of the basic matrix multiplication algorithm. In this paper we continue with analysis of GPU memory hierarchy and corresponding cache memory organization. We give a theoretical analysis why a negative performance impulse appears for specifics problem sizes. The main reason is the cache storage organization, i.e. the negative performance peak appears caused by mapping of matrix elements onto one cache set, instead of using the whole cache. The obtained experimental results prove our theoretical analysis. We also propose a method to avoid situations where performance drawbacks appear.

GPU devices offer great performance when dealing with algorithms that require intense computational resources. A developer can configure the L1 cache memory of the latest GPU Kepler architecture with different cache size and cache set associativity, per Streaming Multiprocessors (SM). The performance of the computation intensive algorithms can be affected by these cache parameters. In this paper, we evaluate the influence of the performance for all possible configurations of L1 cache size and associativity, for dense matrix-matrix multiplication algorithm for various problem sizes. The results show a small impact of various L1 cache memory configurations for the overall performance of the algorithm.