67,483 research outputs found
Receiver Architectures for MIMO-OFDM Based on a Combined VMP-SP Algorithm
Iterative information processing, either based on heuristics or analytical
frameworks, has been shown to be a very powerful tool for the design of
efficient, yet feasible, wireless receiver architectures. Within this context,
algorithms performing message-passing on a probabilistic graph, such as the
sum-product (SP) and variational message passing (VMP) algorithms, have become
increasingly popular.
In this contribution, we apply a combined VMP-SP message-passing technique to
the design of receivers for MIMO-ODFM systems. The message-passing equations of
the combined scheme can be obtained from the equations of the stationary points
of a constrained region-based free energy approximation. When applied to a
MIMO-OFDM probabilistic model, we obtain a generic receiver architecture
performing iterative channel weight and noise precision estimation,
equalization and data decoding. We show that this generic scheme can be
particularized to a variety of different receiver structures, ranging from
high-performance iterative structures to low complexity receivers. This allows
for a flexible design of the signal processing specially tailored for the
requirements of each specific application. The numerical assessment of our
solutions, based on Monte Carlo simulations, corroborates the high performance
of the proposed algorithms and their superiority to heuristic approaches
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Preparing sparse solvers for exascale computing.
Sparse solvers provide essential functionality for a wide variety of scientific applications. Highly parallel sparse solvers are essential for continuing advances in high-fidelity, multi-physics and multi-scale simulations, especially as we target exascale platforms. This paper describes the challenges, strategies and progress of the US Department of Energy Exascale Computing project towards providing sparse solvers for exascale computing platforms. We address the demands of systems with thousands of high-performance node devices where exposing concurrency, hiding latency and creating alternative algorithms become essential. The efforts described here are works in progress, highlighting current success and upcoming challenges. This article is part of a discussion meeting issue 'Numerical algorithms for high-performance computational science'
A Survey on Compiler Autotuning using Machine Learning
Since the mid-1990s, researchers have been trying to use machine-learning
based approaches to solve a number of different compiler optimization problems.
These techniques primarily enhance the quality of the obtained results and,
more importantly, make it feasible to tackle two main compiler optimization
problems: optimization selection (choosing which optimizations to apply) and
phase-ordering (choosing the order of applying optimizations). The compiler
optimization space continues to grow due to the advancement of applications,
increasing number of compiler optimizations, and new target architectures.
Generic optimization passes in compilers cannot fully leverage newly introduced
optimizations and, therefore, cannot keep up with the pace of increasing
options. This survey summarizes and classifies the recent advances in using
machine learning for the compiler optimization field, particularly on the two
major problems of (1) selecting the best optimizations and (2) the
phase-ordering of optimizations. The survey highlights the approaches taken so
far, the obtained results, the fine-grain classification among different
approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our
Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated
quarterly here (Send me your new published papers to be added in the
subsequent version) History: Received November 2016; Revised August 2017;
Revised February 2018; Accepted March 2018
Automated Code Generation for Lattice Quantum Chromodynamics and beyond
We present here our ongoing work on a Domain Specific Language which aims to
simplify Monte-Carlo simulations and measurements in the domain of Lattice
Quantum Chromodynamics. The tool-chain, called Qiral, is used to produce
high-performance OpenMP C code from LaTeX sources. We discuss conceptual issues
and details of implementation and optimization. The comparison of the
performance of the generated code to the well-established simulation software
is also made
GHOST: Building blocks for high performance sparse linear algebra on heterogeneous systems
While many of the architectural details of future exascale-class high
performance computer systems are still a matter of intense research, there
appears to be a general consensus that they will be strongly heterogeneous,
featuring "standard" as well as "accelerated" resources. Today, such resources
are available as multicore processors, graphics processing units (GPUs), and
other accelerators such as the Intel Xeon Phi. Any software infrastructure that
claims usefulness for such environments must be able to meet their inherent
challenges: massive multi-level parallelism, topology, asynchronicity, and
abstraction. The "General, Hybrid, and Optimized Sparse Toolkit" (GHOST) is a
collection of building blocks that targets algorithms dealing with sparse
matrix representations on current and future large-scale systems. It implements
the "MPI+X" paradigm, has a pure C interface, and provides hybrid-parallel
numerical kernels, intelligent resource management, and truly heterogeneous
parallelism for multicore CPUs, Nvidia GPUs, and the Intel Xeon Phi. We
describe the details of its design with respect to the challenges posed by
modern heterogeneous supercomputers and recent algorithmic developments.
Implementation details which are indispensable for achieving high efficiency
are pointed out and their necessity is justified by performance measurements or
predictions based on performance models. The library code and several
applications are available as open source. We also provide instructions on how
to make use of GHOST in existing software packages, together with a case study
which demonstrates the applicability and performance of GHOST as a component
within a larger software stack.Comment: 32 pages, 11 figure
A Multi-GPU Programming Library for Real-Time Applications
We present MGPU, a C++ programming library targeted at single-node multi-GPU
systems. Such systems combine disproportionate floating point performance with
high data locality and are thus well suited to implement real-time algorithms.
We describe the library design, programming interface and implementation
details in light of this specific problem domain. The core concepts of this
work are a novel kind of container abstraction and MPI-like communication
methods for intra-system communication. We further demonstrate how MGPU is used
as a framework for porting existing GPU libraries to multi-device
architectures. Putting our library to the test, we accelerate an iterative
non-linear image reconstruction algorithm for real-time magnetic resonance
imaging using multiple GPUs. We achieve a speed-up of about 1.7 using 2 GPUs
and reach a final speed-up of 2.1 with 4 GPUs. These promising results lead us
to conclude that multi-GPU systems are a viable solution for real-time MRI
reconstruction as well as signal-processing applications in general.Comment: 15 pages, 10 figure
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