8,000 research outputs found
An Asynchronous Parallel Approach to Sparse Recovery
Asynchronous parallel computing and sparse recovery are two areas that have
received recent interest. Asynchronous algorithms are often studied to solve
optimization problems where the cost function takes the form , with a common assumption that each is sparse; that is, each
acts only on a small number of components of . Sparse
recovery problems, such as compressed sensing, can be formulated as
optimization problems, however, the cost functions are dense with respect
to the components of , and instead the signal is assumed to be sparse,
meaning that it has only non-zeros where . Here we address how one
may use an asynchronous parallel architecture when the cost functions are
not sparse in , but rather the signal is sparse. We propose an
asynchronous parallel approach to sparse recovery via a stochastic greedy
algorithm, where multiple processors asynchronously update a vector in shared
memory containing information on the estimated signal support. We include
numerical simulations that illustrate the potential benefits of our proposed
asynchronous method.Comment: 5 pages, 2 figure
CRAFT: A library for easier application-level Checkpoint/Restart and Automatic Fault Tolerance
In order to efficiently use the future generations of supercomputers, fault
tolerance and power consumption are two of the prime challenges anticipated by
the High Performance Computing (HPC) community. Checkpoint/Restart (CR) has
been and still is the most widely used technique to deal with hard failures.
Application-level CR is the most effective CR technique in terms of overhead
efficiency but it takes a lot of implementation effort. This work presents the
implementation of our C++ based library CRAFT (Checkpoint-Restart and Automatic
Fault Tolerance), which serves two purposes. First, it provides an extendable
library that significantly eases the implementation of application-level
checkpointing. The most basic and frequently used checkpoint data types are
already part of CRAFT and can be directly used out of the box. The library can
be easily extended to add more data types. As means of overhead reduction, the
library offers a build-in asynchronous checkpointing mechanism and also
supports the Scalable Checkpoint/Restart (SCR) library for node level
checkpointing. Second, CRAFT provides an easier interface for User-Level
Failure Mitigation (ULFM) based dynamic process recovery, which significantly
reduces the complexity and effort of failure detection and communication
recovery mechanism. By utilizing both functionalities together, applications
can write application-level checkpoints and recover dynamically from process
failures with very limited programming effort. This work presents the design
and use of our library in detail. The associated overheads are thoroughly
analyzed using several benchmarks
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization
Due to their simplicity and excellent performance, parallel asynchronous
variants of stochastic gradient descent have become popular methods to solve a
wide range of large-scale optimization problems on multi-core architectures.
Yet, despite their practical success, support for nonsmooth objectives is still
lacking, making them unsuitable for many problems of interest in machine
learning, such as the Lasso, group Lasso or empirical risk minimization with
convex constraints.
In this work, we propose and analyze ProxASAGA, a fully asynchronous sparse
method inspired by SAGA, a variance reduced incremental gradient algorithm. The
proposed method is easy to implement and significantly outperforms the state of
the art on several nonsmooth, large-scale problems. We prove that our method
achieves a theoretical linear speedup with respect to the sequential version
under assumptions on the sparsity of gradients and block-separability of the
proximal term. Empirical benchmarks on a multi-core architecture illustrate
practical speedups of up to 12x on a 20-core machine.Comment: Appears in Advances in Neural Information Processing Systems 30 (NIPS
2017), 28 page
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