Studies on automatic parallelization for heterogeneous and homogeneous multicore processors

制度:新 ; 報告番号:甲3537号 ; 学位の種類:博士(工学) ; 授与年月日:2012/2/25 ; 早大学位記番号:新587

Accelerating Pattern Matching in Neuromorphic Text Recognition System Using Intel Xeon Phi Coprocessor

Neuromorphic computing systems refer to the computing architecture inspired by the working mechanism of human brains. The rapidly reducing cost and increasing performance of state-of-the-art computing hardware allows large-scale implementation of machine intelligence models with neuromorphic architectures and opens the opportunity for new applications. One such computing hardware is Intel Xeon Phi coprocessor, which delivers over a TeraFLOP of computing power with 61 integrated processing cores. How to efficiently harness such computing power to achieve real time decision and cognition is one of the key design considerations. This work presents an optimized implementation of Brain-State-in-a-Box (BSB) neural network model on the Xeon Phi coprocessor for pattern matching in the context of intelligent text recognition of noisy document images. From a scalability standpoint on a High Performance Computing (HPC) platform we show that efficient workload partitioning and resource management can double the performance of this many-core architecture for neuromorphic applications

Automatic Performance Optimization on Heterogeneous Computer Systems using Manycore Coprocessors

Emerging computer architectures and advanced computing technologies, such as Intel’s Many Integrated Core (MIC) Architecture and graphics processing units (GPU), provide a promising solution to employ parallelism for achieving high performance, scalability and low power consumption. As a result, accelerators have become a crucial part in developing supercomputers. Accelerators usually equip with different types of cores and memory. It will compel application developers to reach challenging performance goals. The added complexity has led to the development of task-based runtime systems, which allow complex computations to be expressed as task graphs, and rely on scheduling algorithms to perform load balancing between all resources of the platforms. Developing good scheduling algorithms, even on a single node, and analyzing them can thus have a very high impact on the performance of current HPC systems. Load balancing strategies, at different levels, will be critical to obtain an effective usage of the heterogeneous hardware and to reduce the impact of communication on energy and performance. Implementing efficient load balancing algorithms, able to manage heterogeneous hardware, can be a challenging task, especially when a parallel programming model for distributed memory architecture. In this paper, we presents several novel runtime approaches to determine the optimal data and task partition on heterogeneous platforms, targeting the Intel Xeon Phi accelerated heterogeneous systems

Генерация потоковых сетей акторов поиска кратчайших путей для параллельной многоядерной реализации

Objectives. The problem of parallelizing computations on multicore systems is considered. On the Floyd – Warshall blocked algorithm of shortest paths search in dense graphs of large size, two types of parallelism are compared: fork-join and network dataflow. Using the CAL programming language, a method of developing actors and an algorithm of generating parallel dataflow networks are proposed. The objective is to improve performance of parallel implementations of algorithms which have the property of partial order of computations on multicore processors.Methods. Methods of graph theory, algorithm theory, parallelization theory and formal language theory are used.Results. Claims about the possibility of reordering calculations in the blocked Floyd – Warshall algorithm are proved, which make it possible to achieve a greater load of cores during algorithm execution. Based on the claims, a method of constructing actors in the CAL language is developed and an algorithm for automatic generation of dataflow CAL networks for various configurations of block matrices describing the lengths of the shortest paths is proposed. It is proved that the networks have the properties of rate consistency, boundedness, and liveness. In actors running in parallel, the order of execution of actions with asynchronous behavior can change dynamically, resulting in efficient use of caches and increased core load. To implement the new features of actors, networks and the method of their generation, a tunable multi-threaded CAL engine has been developed that implements a static dataflow model of computation with bounded sizes of buffers. From the experimental results obtained on four types of multi-core processors it follows that there is an optimal size of the network matrix of actors for which the performance is maximum, and the size depends on the number of cores and the size of graph.Conclusion. It has been shown that dataflow networks of actors are an effective means to parallelize computationally intensive algorithms that describe a partial order of computations over decomposed data. The results obtained on the blocked algorithm of shortest paths search prove that the parallelism of dataflow networks gives higher performance of software implementations on multicore processors in comparison with the fork-join parallelism of OpenMP.Цели. Рассматривается задача распараллеливания вычислений на многоядерных системах. Посредством блочного алгоритма Флойда – Уоршалла поиска кратчайших путей на плотных графах большого размера сравниваются два вида параллелизма: разветвление/слияние и сетевой потоковый. С использованием языка программирования CAL разрабатываются метод построения акторов потока данных и алгоритм генерации параллельных сетей акторов. Целью работы является повышение производительности параллельных сетевых реализаций алгоритмов, обладающих свойством частичного порядка вычислений, на многоядерных процессорах.Методы. Используются методы теории графов, теории алгоритмов, теории распараллеливания, теории формальных языков.Результаты. Доказаны утверждения о возможности переупорядочивания вычислений в блочном алгоритме Флойда – Уоршалла, способствующие повышению загрузки ядер при реализации алгоритма. На основе утверждений разработан метод построения акторов на языке CAL и предложен алгоритм автоматической генерации CAL-сетей потока данных для различных конфигураций матриц блоков, описывающих длины кратчайших путей. Доказано, что сети обладают свойствами согласованности, ограниченности и живучести. В акторах, работающих параллельно, порядок выполнения действий с асинхронным поведением может динамически меняться, что приводит к эффективному использованию кэшей и увеличению загрузки ядер. Для реализации новых возможностей акторов, сетей и метода их генерации разработан настраиваемый многопоточный CAL-движок, реализующий статическую модель потоковых вычислений с ограниченными размерами буферов. Из экспериментальных результатов, полученных на четырех типах многоядерных процессоров, следует, что существует оптимальный размер сетевой матрицы акторов, для которого производительность максимальна, и этот размер зависит от размера графа и количества ядер.Заключение. Показано, что сети акторов потока данных являются эффективным средством распарал-леливания алгоритмов с высокой вычислительной нагрузкой, описывающих частичный порядок вычислений над данными, декомпозированными на части. Результаты, полученные на блочном алгоритме поиска кратчайших путей, показали, что параллелизм сетей потока данных дает более высокую производительность программных реализаций на многоядерных процессорах по сравнению с параллелизмом разветвления/слияния стандарта OpenMP

Scheduling Dynamic Parallelism On Accelerators

Resource management on accelerator based systems is complicated by the disjoint nature of the main CPU and accelerator, which involves separate memory hierarhcies, different degrees of parallelism, and relatively high cost of communicating between them. For applications with irregular parallelism, where work is dynamically created based on other computations, the accelerators may both consume and produce work. To maintain load balance, the accelerators hand work back to the CPU to be scheduled. In this paper we consider multiple approaches for such scheduling problems and use the Cell BE system to demonstrate the different schedulers and the trade-offs between them. Our evaluation is done with both microbenchmarks and two bioinformatics applications (PBPI and RAxML). Our baseline approach uses a standard Linux scheduler on the CPU, possibly with more than one process per CPU. We then consider the addition of cooperative scheduling to the Linux kernel and a user-level work-stealing approach. The two cooperative approaches are able to decrease SPE idle time, by 30 % and 70%, respectively, relative to the baseline scheduler. In both cases we believe the changes required to application level codes, e.g., a program written with MPI processes that use accelerator based compute nodes, is reasonable, although the kernel level approach provides more generality and ease of implementation, but often less performance than work stealing approach