8,210 research outputs found
PENCIL: Towards a Platform-Neutral Compute Intermediate Language for DSLs
We motivate the design and implementation of a platform-neutral compute
intermediate language (PENCIL) for productive and performance-portable
accelerator programming
Supercomputer optimizations for stochastic optimal control applications
Supercomputer optimizations for a computational method of solving stochastic, multibody, dynamic programming problems are presented. The computational method is valid for a general class of optimal control problems that are nonlinear, multibody dynamical systems, perturbed by general Markov noise in continuous time, i.e., nonsmooth Gaussian as well as jump Poisson random white noise. Optimization techniques for vector multiprocessors or vectorizing supercomputers include advanced data structures, loop restructuring, loop collapsing, blocking, and compiler directives. These advanced computing techniques and superconducting hardware help alleviate Bellman's curse of dimensionality in dynamic programming computations, by permitting the solution of large multibody problems. Possible applications include lumped flight dynamics models for uncertain environments, such as large scale and background random aerospace fluctuations
Relay: A New IR for Machine Learning Frameworks
Machine learning powers diverse services in industry including search,
translation, recommendation systems, and security. The scale and importance of
these models require that they be efficient, expressive, and portable across an
array of heterogeneous hardware devices. These constraints are often at odds;
in order to better accommodate them we propose a new high-level intermediate
representation (IR) called Relay. Relay is being designed as a
purely-functional, statically-typed language with the goal of balancing
efficient compilation, expressiveness, and portability. We discuss the goals of
Relay and highlight its important design constraints. Our prototype is part of
the open source NNVM compiler framework, which powers Amazon's deep learning
framework MxNet
GPU accelerated Monte Carlo simulation of Brownian motors dynamics with CUDA
This work presents an updated and extended guide on methods of a proper
acceleration of the Monte Carlo integration of stochastic differential
equations with the commonly available NVIDIA Graphics Processing Units using
the CUDA programming environment. We outline the general aspects of the
scientific computing on graphics cards and demonstrate them with two models of
a well known phenomenon of the noise induced transport of Brownian motors in
periodic structures. As a source of fluctuations in the considered systems we
selected the three most commonly occurring noises: the Gaussian white noise,
the white Poissonian noise and the dichotomous process also known as a random
telegraph signal. The detailed discussion on various aspects of the applied
numerical schemes is also presented. The measured speedup can be of the
astonishing order of about 3000 when compared to a typical CPU. This number
significantly expands the range of problems solvable by use of stochastic
simulations, allowing even an interactive research in some cases.Comment: 21 pages, 5 figures; Comput. Phys. Commun., accepted, 201
Proceedings of the 3rd Workshop on Domain-Specific Language Design and Implementation (DSLDI 2015)
The goal of the DSLDI workshop is to bring together researchers and
practitioners interested in sharing ideas on how DSLs should be designed,
implemented, supported by tools, and applied in realistic application contexts.
We are both interested in discovering how already known domains such as graph
processing or machine learning can be best supported by DSLs, but also in
exploring new domains that could be targeted by DSLs. More generally, we are
interested in building a community that can drive forward the development of
modern DSLs. These informal post-proceedings contain the submitted talk
abstracts to the 3rd DSLDI workshop (DSLDI'15), and a summary of the panel
discussion on Language Composition
Parallel and vector computation for stochastic optimal control applications
A general method for parallel and vector numerical solutions of stochastic dynamic programming problems is described for optimal control of general nonlinear, continuous time, multibody dynamical systems, perturbed by Poisson as well as Gaussian random white noise. Possible applications include lumped flight dynamics models for uncertain environments, such as large scale and background random atmospheric fluctuations. The numerical formulation is highly suitable for a vector multiprocessor or vectorizing supercomputer, and results exhibit high processor efficiency and numerical stability. Advanced computing techniques, data structures, and hardware help alleviate Bellman's curse of dimensionality in dynamic programming computations
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