3,654 research outputs found
Contract-Based General-Purpose GPU Programming
Using GPUs as general-purpose processors has revolutionized parallel
computing by offering, for a large and growing set of algorithms, massive
data-parallelization on desktop machines. An obstacle to widespread adoption,
however, is the difficulty of programming them and the low-level control of the
hardware required to achieve good performance. This paper suggests a
programming library, SafeGPU, that aims at striking a balance between
programmer productivity and performance, by making GPU data-parallel operations
accessible from within a classical object-oriented programming language. The
solution is integrated with the design-by-contract approach, which increases
confidence in functional program correctness by embedding executable program
specifications into the program text. We show that our library leads to modular
and maintainable code that is accessible to GPGPU non-experts, while providing
performance that is comparable with hand-written CUDA code. Furthermore,
runtime contract checking turns out to be feasible, as the contracts can be
executed on the GPU
A Review on Software Architectures for Heterogeneous Platforms
The increasing demands for computing performance have been a reality
regardless of the requirements for smaller and more energy efficient devices.
Throughout the years, the strategy adopted by industry was to increase the
robustness of a single processor by increasing its clock frequency and mounting
more transistors so more calculations could be executed. However, it is known
that the physical limits of such processors are being reached, and one way to
fulfill such increasing computing demands has been to adopt a strategy based on
heterogeneous computing, i.e., using a heterogeneous platform containing more
than one type of processor. This way, different types of tasks can be executed
by processors that are specialized in them. Heterogeneous computing, however,
poses a number of challenges to software engineering, especially in the
architecture and deployment phases. In this paper, we conduct an empirical
study that aims at discovering the state-of-the-art in software architecture
for heterogeneous computing, with focus on deployment. We conduct a systematic
mapping study that retrieved 28 studies, which were critically assessed to
obtain an overview of the research field. We identified gaps and trends that
can be used by both researchers and practitioners as guides to further
investigate the topic
A Survey of Techniques For Improving Energy Efficiency in Embedded Computing Systems
Recent technological advances have greatly improved the performance and
features of embedded systems. With the number of just mobile devices now
reaching nearly equal to the population of earth, embedded systems have truly
become ubiquitous. These trends, however, have also made the task of managing
their power consumption extremely challenging. In recent years, several
techniques have been proposed to address this issue. In this paper, we survey
the techniques for managing power consumption of embedded systems. We discuss
the need of power management and provide a classification of the techniques on
several important parameters to highlight their similarities and differences.
This paper is intended to help the researchers and application-developers in
gaining insights into the working of power management techniques and designing
even more efficient high-performance embedded systems of tomorrow
PyCUDA and PyOpenCL: A Scripting-Based Approach to GPU Run-Time Code Generation
High-performance computing has recently seen a surge of interest in
heterogeneous systems, with an emphasis on modern Graphics Processing Units
(GPUs). These devices offer tremendous potential for performance and efficiency
in important large-scale applications of computational science. However,
exploiting this potential can be challenging, as one must adapt to the
specialized and rapidly evolving computing environment currently exhibited by
GPUs. One way of addressing this challenge is to embrace better techniques and
develop tools tailored to their needs. This article presents one simple
technique, GPU run-time code generation (RTCG), along with PyCUDA and PyOpenCL,
two open-source toolkits that support this technique.
In introducing PyCUDA and PyOpenCL, this article proposes the combination of
a dynamic, high-level scripting language with the massive performance of a GPU
as a compelling two-tiered computing platform, potentially offering significant
performance and productivity advantages over conventional single-tier, static
systems. The concept of RTCG is simple and easily implemented using existing,
robust infrastructure. Nonetheless it is powerful enough to support (and
encourage) the creation of custom application-specific tools by its users. The
premise of the paper is illustrated by a wide range of examples where the
technique has been applied with considerable success.Comment: Submitted to Parallel Computing, Elsevie
cphVB: A System for Automated Runtime Optimization and Parallelization of Vectorized Applications
Modern processor architectures, in addition to having still more cores, also
require still more consideration to memory-layout in order to run at full
capacity. The usefulness of most languages is deprecating as their
abstractions, structures or objects are hard to map onto modern processor
architectures efficiently.
The work in this paper introduces a new abstract machine framework, cphVB,
that enables vector oriented high-level programming languages to map onto a
broad range of architectures efficiently. The idea is to close the gap between
high-level languages and hardware optimized low-level implementations. By
translating high-level vector operations into an intermediate vector bytecode,
cphVB enables specialized vector engines to efficiently execute the vector
operations.
The primary success parameters are to maintain a complete abstraction from
low-level details and to provide efficient code execution across different,
modern, processors. We evaluate the presented design through a setup that
targets multi-core CPU architectures. We evaluate the performance of the
implementation using Python implementations of well-known algorithms: a jacobi
solver, a kNN search, a shallow water simulation and a synthetic stencil
simulation. All demonstrate good performance
- …