We present a study of three important kernels that occur frequently in iterative statistical applications: Multi-Dimensional Scaling (MDS), PageRank, and K-Means. We implemented each kernel using OpenCL and evaluated their performance on NVIDIA Tesla and NVIDIA Fermi GPGPU cards using dedicated hardware, and in the case of Fermi, also on the Amazon EC2 cloud-computing environment. By examining ...

Shared memory multicore processor technology is pervasive in mainstream computing. This new architecture challenges programmers to write code that scales over these many cores to exploit the full computational power of these machines. OpenMP and Intel Threading Building Blocks (TBB) are two of the popular frameworks used to program these architectures. Recently, OpenCL has been defined as a sta...

Modern SoCs are getting increasingly heterogeneous with a combination of multi-core architectures and hardware accelerators to speed up the execution of computeintensive tasks at considerably lower power consumption. Modern FPGAs, due to their reasonable execution speed and comparatively lower power consumption, are strong competitors to the traditional GPU based accelerators. High-level Synthe...

Computer vision applications constitute one of the key drivers for embedded many-core architectures. In order to exploit the full potential of such systems, a balance between computation and communication is critical, but many computer vision algorithms present a highly datadependent behavior that complexifies this task. To enable application performance optimization, the development environmen...

We introduce SparkCL, an open source unified programming framework based on Java, OpenCL and the Apache Spark framework. The motivation behind this work is to bring unconventional compute cores such as FPGAs/GPUs/APUs/DSPs and future core types into mainstream programming use. The framework allows equal treatment of different computing devices under the Spark framework and introduces the abilit...

In this paper we present out experiences with the implementation of an object detector using OpenCL. With this implementation we fullfil the need for fast and robust object detection, necessary in many applications in multiple domains (surveillance, traffic, image retrieval, ...). The algorithm lends itself to be implemented in a parallel way. We exploit this opportunity by implementing it on a...

The use of heterogeneous devices is becoming increasingly widespread. Their main drawback is their low programmability due to the large amount of details that must be handled. Another important problem is the reduced code portability, as most of the tools to program them are vendor or device-specific. The exception to this observation is OpenCL, which largely suffers from the reduced programmab...

Sparse linear systems are found in a variety of scientific and engineering problems. In VLSI CAD tools, DC circuit analysis creates large, sparse systems represented by matrices and vectors. The algorithms designed to solve these systems are known to be quite time consuming and many previous attempts have been made to parallelize them. Graphics cards have evolved from specialized devices into m...

We present a novel framework for the simultaneous development for different massively parallel platforms. Currently, our framework supports CUDA and OpenCL but it can be easily adapted to other programming languages. The main idea is to provide an easy-to-use abstraction layer that encapsulates the calls of own parallel device code as well as library functions. With our framework the code has t...

In this paper we introduce IPMACC, a framework for translating OpenACC applications to CUDA or OpenCL. IPMACC is composed of set of translators translating OpenACC for C applications to CUDA or OpenCL. The framework uses the system compiler (e.g. nvcc) for generating final accelerator’s binary. The framework can be used for extending the OpenACC API, executing OpenACC applications, or obtaining...