Search results for "Data parallelism"
showing 6 items of 6 documents
Parallel algorithms for large-scale biological sequence alignment on Xeon-Phi based clusters
2016
Computing alignments between two or more sequences are common operations frequently performed in computational molecular biology. The continuing growth of biological sequence databases establishes the need for their efficient parallel implementation on modern accelerators. This paper presents new approaches to high performance biological sequence database scanning with the Smith-Waterman algorithm and the first stage of progressive multiple sequence alignment based on the ClustalW heuristic on a Xeon Phi-based compute cluster. Our approach uses a three-level parallelization scheme to take full advantage of the compute power available on this type of architecture; i.e. cluster-level data par…
Portable Video Supercomputing
2004
As inexpensive imaging chips and wireless telecommunications are incorporated into an increasing array, of portable products, the need for high efficiency, high throughput embedded processing will become an important challenge in computer architecture. Videocentric applications, such wireless videoconferencing, real-time video enhancement and analysis, and new, immersive modes of distance education, will exceed the computational capabilities of current microprocessor and digital signal processor (DSP) architectures. A new class of embedded computers, portable video supercomputers, will combine supercomputer performance with the energy efficiency required for deployment in portable systems. …
SWAPHI-LS: Smith-Waterman Algorithm on Xeon Phi coprocessors for Long DNA Sequences
2014
As an optimal method for sequence alignment, the Smith-Waterman (SW) algorithm is widely used. Unfortunately, this algorithm is computationally demanding, especially for long sequences. This has motivated the investigation of its acceleration on a variety of high-performance computing platforms. However, most work in the literature is only suitable for short sequences. In this paper, we present SWAPHI-LS, the first parallel SW algorithm exploiting emerging Xeon Phi coprocessors to accelerate the alignment of long DNA sequences. In SWAPHI-LS, we have investigated three parallelization approaches (naive, tiled, and distributed) in order to deeply explore the inherent parallelism within Xeon P…
Elementary transformation analysis for Array-OL
2009
Array-OL is a high-level specification language dedicated to the definition of multidimentional intensive signal processing applications. It allows to specify both the task parallelism and the data parallelism of these applications on focusing on their complex multidimensional data access patterns. Several tools exist for implementing an Array-OL specification as a data parallel program. While Array-OL can be used directly, it is often convenient to be able to deduce part of the specification from a sequential version of the application. This paper proposes such an analysis and examines its feasibility and its limits.
Accelerating large-scale biological database search on Xeon Phi-based neo-heterogeneous architectures
2015
In this paper we present new parallelization techniques for searching large-scale biological sequence databases with the Smith-Waterman algorithm on Xeon Phi-based neoheterogenous architectures. In order to make full use of the compute power of both the multi-core CPU and the many-core Xeon Phi hardware, we use a collaborative computing scheme as well as hybrid parallelism. At the CPU side, we employ SSE intrinsics and multi-threading to implement SIMD parallelism. At the Xeon Phi side, we use Knights Corner vector instructions to gain more data parallelism. We have presented two dynamic task distribution schemes (thread level and device level) in order to achieve better load balancing. Fur…
Improving big-data automotive applications performance through adaptive resource allocation
2019
In automotive applications, connected vehicles (CVs) can collect various information (external temperature, speed, location, etc.) and send them to a central infrastructure for exploitation in a wide range of applications: Eco-Driving, fleet management, environmental monitoring, etc. Such applications are known to generate a massive volume of data that is processed in real or near real time (i.e., data streams) depending on the target application requirements. To handle this data volume, big data architectures, based on stream computing paradigm, are usually adopted. Within this paradigm, data are continuously processed by a set of operators (elementary operations) instances. Further, a str…