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Anecdotal evidence suggests that the variety of Big data is one of the most challenging problems in Computer Science research today [Stonebraker, 2012], [Ou et al., 2017], [Guo et al., 2016], [Bai et al., 2016]. First, Big data comes at us from a myriad of data sources, hence its shape and flavor differ. Second, hundreds of data management systems which work with Big data support different APIs and...
The increase in the size of the data used in natural language processing activities brings with it time and space constraints. Thus, it is important to both store and access data efficiently. This study includes experiments for storing the term-document index, which will be used in a natural language processing project, effectively in memory. For this purpose, the indexed data is compressed using...
Variable Pipeline Cool Mega Array (VPCMA) is an low power Coarse Grained Reconfigurable Architecture (CGRA) based on the concept of CMA (Cool Mega Array). It implements a pipeline structure that can be configured depending on performance requirements, and the silicon on thin buried oxide (SOTB) technology that allows to control its body bias voltage to balance performance and leakage power. In this...
A heterogeneous memory system (HMS) consists of multiple memory components with different properties. GPU is a representative architecture with HMS. It is challenging to decide optimal placement of data objects on HMS because of the large exploration space and complicated memory hierarchy on HMS. In this paper, we introduce performance modeling techniques to predict performance of various data placements...
We describe CPU and GPU implementations of parallel triangle-counting and k-truss identification in the Galois and IrGL systems. Both systems are based on a graph-centric abstraction called the operator formulation of algorithms. Depending on the input graph, our implementations are two to three orders of magnitude faster than the reference implementations provided by the IEEE HPEC static graph challenge.
This paper presents a technique to increase the data throughput in Multiple Input Multiple Output (MIMO) systems. The technique uses a meta-heuristic to optimize the data throughput and to choose the best solution. The optimization is based on Genetic Algorithms (GA), with the objective of finding out the best antenna configuration to achieve the highest data throughput from the variation of the distance...
The photovoltaic (PV) array is one of the main components of the PV system, and the accuracy of the PV array model is directly related to the validity of the simulation results. The parameters of the PV array may change with the operation conditions. Therefore, it is important to identify the parameters of the PV array model according to the measured data. In this paper, the conventional four-parameter...
The objective of the work is to provide a recommender system through optimization. The users place orders to suppliers through email, requesting preferences based on cost and delivery date. Optimization algorithms are formulated to provide an optimal mix of the products to the users based on the cost and speed of delivery. The algorithm takes the availability and cost of the products with the suppliers...
We describe a strategy for code modernisation of Gadget, a widely used community code for computational astrophysics. The focus of this work is on node-level performance optimisation, targeting current multi/many-core Intel® architectures. We identify and isolate a sample code kernel, which is representative of a typical Smoothed Particle Hydrodynamics (SPH) algorithm. The code modifications include...
The contribution of the present work relies on an innovative and judicious combination of several optimization techniques for achieving high performance when using automatic vectorization and hybrid MPI/OpenMP parallelism in a Particle-in-Cell (PIC) code. The domain of application is plasma physics: the code simulates 2d2v Vlasov-Poisson systems on Cartesian grids with periodic boundary conditions...
We explore the use of synthetic benchmarks for the training phase of machine-learning-based automatic performance tuning. We focus on the problem of predicting if the use of local memory on a GPU is beneficial for caching a single target array in a GPU kernel. We show that the use of only 13 real benchmarks leads to poor prediction accuracy (about to 58%) of the 13 leave-one-out models trained using...
The Finite Difference Time Domain (FDTD) Method is used for full-wave electromagnetic (EM) simulations. FDTD is computationally intensive with performance depending critically on architecture-specific optimizations that have become more challenging given the rapidly changing architectures in modern high-performance computing platforms. We examine a templated meta-programming technique to implement...
In this paper we discuss the feasibility of an online failure detection algorithm for large linear arrays for massive MIMO applications. By means of a numerical optimization of the position of few near field probes, a good failure detection performance has been verified.
Parallel patterns (e.g., map, reduce) have gained traction as an abstraction for targeting parallel accelerators and are a promising answer to the performance portability problem. However, compiling high-level programs into efficient low-level parallel code is challenging. Current approaches start from a high-level parallel IR and proceed to emit GPU code directly in one big step. Fixed strategies...
Today's caches tightly couple data with metadata (Address Tags) at the cache line granularity. The co-location of data and its identifying metadata means that they require multiple approaches to locate data (associative way searches and level-by-level searches), evict data (coherent writebacks buffers and associative level-by-level searches) and keep data coherent (directory indirections and associative...
Convolutional neural networks (CNNs) have been widely applied in many deep learning applications. In recent years, the FPGA implementation for CNNs has attracted much attention because of its high performance and energy efficiency. However, existing implementations have difficulty to fully leverage the computation power of the latest FPGAs. In this paper we implement CNN on an FPGA using a systolic...
In most developing countries, for the proper utilization of solar energy the system designed should be optimized. This paper presents the design and analysis of photovoltaic (PV) system to supply electricity. A stand-alone optimization simulation model is developed using the HOMER software. The radiation data for Baru Sahib, H.P. is collected for the every month of the year. Simulation model is used...
General Purpose Graphic Processing Unit(GPGPU) is used widely for achieving high performance or high throughput in parallel programming. This capability of GPGPUs is very famous in the new era and mostly used for scientific computing which requires more processing power than normal personal computers. Therefore, most of the programmers, researchers and industry use this new concept for their work...
Graph processing is used in many fields of science such as sociology, risk prediction or biology. Although analysis of graphs is important it also poses numerous challenges especially for large graphs which have to be processed on multicore systems. In this paper, we present PGAS (Partitioned Global Address Space) version of the level-synchronous BFS (Breadth First Search) algorithm and its implementation...
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