43,166 research outputs found
OpenCL Actors - Adding Data Parallelism to Actor-based Programming with CAF
The actor model of computation has been designed for a seamless support of
concurrency and distribution. However, it remains unspecific about data
parallel program flows, while available processing power of modern many core
hardware such as graphics processing units (GPUs) or coprocessors increases the
relevance of data parallelism for general-purpose computation.
In this work, we introduce OpenCL-enabled actors to the C++ Actor Framework
(CAF). This offers a high level interface for accessing any OpenCL device
without leaving the actor paradigm. The new type of actor is integrated into
the runtime environment of CAF and gives rise to transparent message passing in
distributed systems on heterogeneous hardware. Following the actor logic in
CAF, OpenCL kernels can be composed while encapsulated in C++ actors, hence
operate in a multi-stage fashion on data resident at the GPU. Developers are
thus enabled to build complex data parallel programs from primitives without
leaving the actor paradigm, nor sacrificing performance. Our evaluations on
commodity GPUs, an Nvidia TESLA, and an Intel PHI reveal the expected linear
scaling behavior when offloading larger workloads. For sub-second duties, the
efficiency of offloading was found to largely differ between devices. Moreover,
our findings indicate a negligible overhead over programming with the native
OpenCL API.Comment: 28 page
Distributed Sparse Signal Recovery For Sensor Networks
We propose a distributed algorithm for sparse signal recovery in sensor
networks based on Iterative Hard Thresholding (IHT). Every agent has a set of
measurements of a signal x, and the objective is for the agents to recover x
from their collective measurements at a minimal communication cost and with low
computational complexity. A naive distributed implementation of IHT would
require global communication of every agent's full state in each iteration. We
find that we can dramatically reduce this communication cost by leveraging
solutions to the distributed top-K problem in the database literature.
Evaluations show that our algorithm requires up to three orders of magnitude
less total bandwidth than the best-known distributed basis pursuit method
Inner product computation for sparse iterative solvers on\ud distributed supercomputer
Recent years have witnessed that iterative Krylov methods without re-designing are not suitable for distribute supercomputers because of intensive global communications. It is well accepted that re-engineering Krylov methods for prescribed computer architecture is necessary and important to achieve higher performance and scalability. The paper focuses on simple and practical ways to re-organize Krylov methods and improve their performance for current heterogeneous distributed supercomputers. In construct with most of current software development of Krylov methods which usually focuses on efficient matrix vector multiplications, the paper focuses on the way to compute inner products on supercomputers and explains why inner product computation on current heterogeneous distributed supercomputers is crucial for scalable Krylov methods. Communication complexity analysis shows that how the inner product computation can be the bottleneck of performance of (inner) product-type iterative solvers on distributed supercomputers due to global communications. Principles of reducing such global communications are discussed. The importance of minimizing communications is demonstrated by experiments using up to 900 processors. The experiments were carried on a Dawning 5000A, one of the fastest and earliest heterogeneous supercomputers in the world. Both the analysis and experiments indicates that inner product computation is very likely to be the most challenging kernel for inner product-based iterative solvers to achieve exascale
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