Efficient GPU Implementation of Affine Index Permutations on Arrays

Optimal usage of the memory system is a key element of fast GPU algorithms. Unfortunately many common algorithms fail in this regard despite exhibiting great regularity in memory access patterns. In this paper we propose efficient kernels to permute the elements of an array. We handle a class of permutations known as Bit Matrix Multiply Complement (BMMC) permutations, for which we design kernels of speed comparable to that of a simple array copy. This is a first step towards implementing a set of array combinators based on these permutations

Efficient GPU implementation of a class of array permutations

Optimal usage of the memory system is a key element of fast GPU algorithms. Unfortunately many common algorithms fail in this regard despite exhibiting great regularity in memory access patterns. In this paper we propose efficient kernels to permute the elements of an array, which can be used to improve the access patterns of many algorithms. We handle a class of permutations known as Bit Matrix Multiply Complement (BMMC) permutations, for which we design kernels of speed comparable to that of a simple array copy. This is a first step towards implementing a set of array combinators based on these permutations.Comment: Submitted to ACM SIGPLAN International Workshop on Functional High-Performance and Numerical Computing 202

Efficient similarity search on multimedia databases

Manipulating and retrieving multimedia data has received increasing attention with the advent of cloud storage facilities. The ability of querying by similarity over large data collections is mandatory to improve storage and user interfaces. But, all of them are expensive operations to solve only in CPU; thus, it is convenient to take into account High Performance Computing (HPC) techniques in their solutions. The Graphics Processing Unit (GPU) as an alternative HPC device has been increasingly used to speedup certain computing processes. This work introduces a pure GPU architecture to build the Permutation Index and to solve approximate similarity queries on multimedia databases. The empirical results of each implementation have achieved different level of speedup which are related with characteristics of GPU and the particular database used.Eje: Workshop Bases de datos y minería de datos (WBDDM)Red de Universidades con Carreras en Informática (RedUNCI

Efficient similarity search on multimedia databases

Manipulating and retrieving multimedia data has received increasing attention with the advent of cloud storage facilities. The ability of querying by similarity over large data collections is mandatory to improve storage and user interfaces. But, all of them are expensive operations to solve only in CPU; thus, it is convenient to take into account High Performance Computing (HPC) techniques in their solutions. The Graphics Processing Unit (GPU) as an alternative HPC device has been increasingly used to speedup certain computing processes. This work introduces a pure GPU architecture to build the Permutation Index and to solve approximate similarity queries on multimedia databases. The empirical results of each implementation have achieved different level of speedup which are related with characteristics of GPU and the particular database used.Eje: Workshop Bases de datos y minería de datos (WBDDM)Red de Universidades con Carreras en Informática (RedUNCI

Evaluating tradeoff between recall and perfomance of GPU permutation index

Query-by-content, by means of similarity search, is a fundamental operation for applications that deal with multimedia data. For this kind of query it is meaningless to look for elements exactly equal to a given one as query. Instead, we need to measure the dissimilarity between the query object and each database object. This search problem can be formalized with the concept of metric space. In this scenario, the search efficiency is understood as minimizing the number of distance calculations required to answer them. Building an index can be a solution, but with very large metric databases is not enough, it is also necessary to speed up the queries by using high performance computing, as GPU, and in some cases is reasonable to accept a fast answer although it was inexact. In this work we evaluate the tradeoff between the answer quality and time performance of our implementation of Permutation Index, on a pure GPU architecture, used to solve in parallel multiple approximate similarity searches on metric databases.WPDP- XIII Workshop procesamiento distribuido y paraleloRed de Universidades con Carreras en Informática (RedUNCI

Improving the Performance and Energy Efficiency of GPGPU Computing through Adaptive Cache and Memory Management Techniques

Department of Computer Science and EngineeringAs the performance and energy efficiency requirement of GPGPUs have risen, memory management techniques of GPGPUs have improved to meet the requirements by employing hardware caches and utilizing heterogeneous memory. These techniques can improve GPGPUs by providing lower latency and higher bandwidth of the memory. However, these methods do not always guarantee improved performance and energy efficiency due to the small cache size and heterogeneity of the memory nodes. While prior works have proposed various techniques to address this issue, relatively little work has been done to investigate holistic support for memory management techniques. In this dissertation, we analyze performance pathologies and propose various techniques to improve memory management techniques. First, we investigate the effectiveness of advanced cache indexing (ACI) for high-performance and energy-efficient GPGPU computing. Specifically, we discuss the designs of various static and adaptive cache indexing schemes and present implementation for GPGPUs. We then quantify and analyze the effectiveness of the ACI schemes based on a cycle-accurate GPGPU simulator. Our quantitative evaluation shows that ACI schemes achieve significant performance and energy-efficiency gains over baseline conventional indexing scheme. We also analyze the performance sensitivity of ACI to key architectural parameters (i.e., capacity, associativity, and ICN bandwidth) and the cache indexing latency. We also demonstrate that ACI continues to achieve high performance in various settings. Second, we propose IACM, integrated adaptive cache management for high-performance and energy-efficient GPGPU computing. Based on the performance pathology analysis of GPGPUs, we integrate state-of-the-art adaptive cache management techniques (i.e., cache indexing, bypassing, and warp limiting) in a unified architectural framework to eliminate performance pathologies. Our quantitative evaluation demonstrates that IACM significantly improves the performance and energy efficiency of various GPGPU workloads over the baseline architecture (i.e., 98.1% and 61.9% on average, respectively) and achieves considerably higher performance than the state-of-the-art technique (i.e., 361.4% at maximum and 7.7% on average). Furthermore, IACM delivers significant performance and energy efficiency gains over the baseline GPGPU architecture even when enhanced with advanced architectural technologies (e.g., higher capacity, associativity). Third, we propose bandwidth- and latency-aware page placement (BLPP) for GPGPUs with heterogeneous memory. BLPP analyzes the characteristics of a application and determines the optimal page allocation ratio between the GPU and CPU memory. Based on the optimal page allocation ratio, BLPP dynamically allocate pages across the heterogeneous memory nodes. Our experimental results show that BLPP considerably outperforms the baseline and state-of-the-art technique (i.e., 13.4% and 16.7%) and performs similar to the static-best version (i.e., 1.2% difference), which requires extensive offline profiling.clos

Fast algorithm for real-time rings reconstruction

The GAP project is dedicated to study the application of GPU in several contexts in which real-time response is important to take decisions. The definition of real-time depends on the application under study, ranging from answer time of μs up to several hours in case of very computing intensive task. During this conference we presented our work in low level triggers [1] [2] and high level triggers [3] in high energy physics experiments, and specific application for nuclear magnetic resonance (NMR) [4] [5] and cone-beam CT [6]. Apart from the study of dedicated solution to decrease the latency due to data transport and preparation, the computing algorithms play an essential role in any GPU application. In this contribution, we show an original algorithm developed for triggers application, to accelerate the ring reconstruction in RICH detector when it is not possible to have seeds for reconstruction from external trackers

Energy-aware scheduling in heterogeneous computing systems

In the last decade, the grid computing systems emerged as useful provider of the computing power required for solving complex problems. The classic formulation of the scheduling problem in heterogeneous computing systems is NP-hard, thus approximation techniques are required for solving real-world scenarios of this problem. This thesis tackles the problem of scheduling tasks in a heterogeneous computing environment in reduced execution times, considering the schedule length and the total energy consumption as the optimization objectives. An efficient multithreading local search algorithm for solving the multi-objective scheduling problem in heterogeneous computing systems, named MEMLS, is presented. The proposed method follows a fully multi-objective approach, applying a Pareto-based dominance search that is executed in parallel by using several threads. The experimental analysis demonstrates that the new multithreading algorithm outperforms a set of fast and accurate two-phase deterministic heuristics based on the traditional MinMin. The new ME-MLS method is able to achieve significant improvements in both makespan and energy consumption objectives in reduced execution times for a large set of testbed instances, while exhibiting very good scalability. The ME-MLS was evaluated solving instances comprised of up to 2048 tasks and 64 machines. In order to scale the dimension of the problem instances even further and tackle large-sized problem instances, the Graphical Processing Unit (GPU) architecture is considered. This line of future work has been initially tackled with the gPALS: a hybrid CPU/GPU local search algorithm for efficiently tackling a single-objective heterogeneous computing scheduling problem. The gPALS shows very promising results, being able to tackle instances of up to 32768 tasks and 1024 machines in reasonable execution times.En la última década, los sistemas de computación grid se han convertido en útiles proveedores de la capacidad de cálculo necesaria para la resolución de problemas complejos. En su formulación clásica, el problema de la planificación de tareas en sistemas heterogéneos es un problema NP difícil, por lo que se requieren técnicas de resolución aproximadas para atacar instancias de tamaño realista de este problema. Esta tesis aborda el problema de la planificación de tareas en sistemas heterogéneos, considerando el largo de la planificación y el consumo energético como objetivos a optimizar. Para la resolución de este problema se propone un algoritmo de búsqueda local eficiente y multihilo. El método propuesto se trata de un enfoque plenamente multiobjetivo que consiste en la aplicación de una búsqueda basada en dominancia de Pareto que se ejecuta en paralelo mediante el uso de varios hilos de ejecución. El análisis experimental demuestra que el algoritmo multithilado propuesto supera a un conjunto de heurísticas deterministas rápidas y e caces basadas en el algoritmo MinMin tradicional. El nuevo método, ME-MLS, es capaz de lograr mejoras significativas tanto en el largo de la planificación y como en consumo energético, en tiempos de ejecución reducidos para un gran número de casos de prueba, mientras que exhibe una escalabilidad muy promisoria. El ME-MLS fue evaluado abordando instancias de hasta 2048 tareas y 64 máquinas. Con el n de aumentar la dimensión de las instancias abordadas y hacer frente a instancias de gran tamaño, se consideró la utilización de la arquitectura provista por las unidades de procesamiento gráfico (GPU). Esta línea de trabajo futuro ha sido abordada inicialmente con el algoritmo gPALS: un algoritmo híbrido CPU/GPU de búsqueda local para la planificación de tareas en en sistemas heterogéneos considerando el largo de la planificación como único objetivo. La evaluación del algoritmo gPALS ha mostrado resultados muy prometedores, siendo capaz de abordar instancias de hasta 32768 tareas y 1024 máquinas en tiempos de ejecución razonables

A GPU implementation of the Correlation Technique for Real-time Fourier Domain Pulsar Acceleration Searches

The study of binary pulsars enables tests of general relativity. Orbital motion in binary systems causes the apparent pulsar spin frequency to drift, reducing the sensitivity of periodicity searches. Acceleration searches are methods that account for the effect of orbital acceleration. Existing methods are currently computationally expensive, and the vast amount of data that will be produced by next generation instruments such as the Square Kilometre Array (SKA) necessitates real-time acceleration searches, which in turn requires the use of High Performance Computing (HPC) platforms. We present our implementation of the Correlation Technique for the Fourier Domain Acceleration Search (FDAS) algorithm on Graphics Processor Units (GPUs). The correlation technique is applied as a convolution with multiple Finite Impulse Response filters in the Fourier domain. Two approaches are compared: the first uses the NVIDIA cuFFT library for applying Fast Fourier Transforms (FFTs) on the GPU, and the second contains a custom FFT implementation in GPU shared memory. We find that the FFT shared memory implementation performs between 1.5 and 3.2 times faster than our cuFFT-based application for smaller but sufficient filter sizes. It is also 4 to 6 times faster than the existing GPU and OpenMP implementations of FDAS. This work is part of the AstroAccelerate project, a many-core accelerated time-domain signal processing library for radio astronomy.Comment: 20 pages, 9 figures. Accepted for publication in ApJ