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Posts

Feb, 10

FSCL: Homogeneous programming, scheduling and execution on heterogeneous platforms

The last few years has seen activity towards programming models, languages and frameworks to address the increasingly wide range and broad availability of heterogeneous computing resources through raised programming abstraction and portability across different platforms. The effort spent in simplifying parallel programming across heterogeneous platforms is often outweighed by the need for low-level control over […]
Feb, 10

GPU-accelerated HMM for Speech Recognition

Speech recognition is used in a wide range of applications and devices such as mobile phones, in-car entertainment systems and web-based services. Hidden Markov Models (HMMs) is one of the most popular algorithmic approaches applied in speech recognition. Training and testing a HMM is computationally intensive and time-consuming. Running multiple applications concurrently with speech recognition […]
Feb, 10

Analysis and Modeling of the Timing Behavior of GPU Architectures

Graphics processing units (GPUs) offer massive parallelism. Since a couple of years GPUs can also be used for more general purpose applications; a wide variety of applications can be accelerated efficiently with the use of the CUDA and OpenCL programming models. Real-time systems frequently use many sensors that produce a big amount of data. GPUs […]
Feb, 10

Patterns and Rewrite Rules for Systematic Code Generation (From High-Level Functional Patterns to High-Performance OpenCL Code)

Computing systems have become increasingly complex with the emergence of heterogeneous hardware combining multicore CPUs and GPUs. These parallel systems exhibit tremendous computational power at the cost of increased programming effort. This results in a tension between achieving performance and code portability. Code is either tuned using device-specific optimizations to achieve maximum performance or is […]
Feb, 10

CAVE-CL: An OpenCL version of the package for detection and quantitative analysis of internal cavities in a system of overlapping balls: application to proteins

Here we present the revised and newly rewritten version of our earlier published CAVE package [J. Busa et al., Comput. Phys. Commun. 181 (2010) 2116] which was originally written in FORTRAN. The package has been rewritten in C language, the algorithm has been parallelized and implemented using OpenCL. This makes the program convenient to run […]
Feb, 9

A Survey of Architectural Techniques For DRAM Power Management

Recent trends of CMOS technology scaling and wide-spread use of multicore processors have dramatically increased the power consumption of main memory. It has been estimated that modern data-centers spend more than 30% of their total power consumption in main memory alone. This excessive power dissipation has created the problem of “memory power wall”; which has […]
Feb, 9

FIR filtering and AES encryption with OpenCL 2.0

OpenCL has become a popular standard to leverage the unique power/performance opportunities found on heterogeneous systems. In this short contribution, we evaluate the latest parallel programming features supported in the OpenCL 2.0 standard. We explore using shared virtual memory and dynamic parallelism to accelerate two example applications.
Feb, 9

Speech Recognition on Modern Graphic Processing Units

Speech Recognition run on Graphic Processing Units (GPUs) has shown some promising performance improvements ranging 2-10x speedups when compare to execution on CPUs. GPU has continued to introduce new programming features, such as Dynamic Parallelism and Hyper-Q, that could further benefit Speech Recognition processing. In this paper we describe a framework developed at Northeastern describing […]
Feb, 9

Fast Subgraph Matching on Large Graphs using Graphics Processors

Subgraph matching is the task of finding all matches of a query graph in a large data graph, which is known as an NP-complete problem. Many algorithms are proposed to solve this problem using CPUs. In recent years, Graphics Processing Units (GPUs) have been adopted to accelerate fundamental graph operations such as breadth-first search and […]
Feb, 9

Sparse Matrix-Vector Multiplication on GPU

Sparse Matrix-Vector multiplication (SpMV) is one of the key operations in linear algebra. Overcoming thread divergence, load imbalance and un-coalesced and indirect memory access due to sparsity and irregularity are challenges to optimizing SpMV on GPUs. This dissertation develops solutions that address these challenges effectively. The first part of this dissertation focuses on a new […]
Feb, 9

Fine-Tuning Vectorization and Memory Traffic on Intel Xeon Phi Coprocessors: LU Decomposition of Small Matrices

Common techniques for fine-tuning the performance of automatically vectorized loops in applications for Intel Xeon Phi coprocessors are discussed. These techniques include strength reduction, regularizing the vectorization pattern, data alignment and aligned data hint, and pointer disambiguation. In addition, the loop tiling technique of memory traffic tuning is shown. The optimization methods are illustrated on […]
Feb, 8

Power Management Techniques for Data Centers: A Survey

With growing use of internet and exponential growth in amount of data to be stored and processed (known as ‘big data’), the size of data centers has greatly increased. This, however, has resulted in significant increase in the power consumption of the data centers. For this reason, managing power consumption of data centers has become […]

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