An Efficient Summation Algorithm for the Accuracy, Convergence and
Reproducibility of Parallel Numerical Methods
- URL: http://arxiv.org/abs/2205.05339v1
- Date: Wed, 11 May 2022 08:31:48 GMT
- Title: An Efficient Summation Algorithm for the Accuracy, Convergence and
Reproducibility of Parallel Numerical Methods
- Authors: Farah Benmouhoub (UPVD), Pierre-Lo\"ic Garoche (ENAC), Matthieu Martel
(UPVD)
- Abstract summary: We have introduced a new parallel algorithm for summing a sequence of floating-point numbers.
This algorithm which scales up easily with the number of processors, adds numbers of the same exponents first.
In this article, our main contribution is an extensive analysis of its efficiency with respect to several properties.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Nowadays, parallel computing is ubiquitous in several application fields,
both in engineering and science. The computations rely on the floating-point
arithmetic specified by the IEEE754 Standard. In this context, an elementary
brick of computation, used everywhere, is the sum of a sequence of numbers.
This sum is subject to many numerical errors in floating-point arithmetic. To
alleviate this issue, we have introduced a new parallel algorithm for summing a
sequence of floating-point numbers. This algorithm which scales up easily with
the number of processors, adds numbers of the same exponents first. In this
article, our main contribution is an extensive analysis of its efficiency with
respect to several properties: accuracy, convergence and reproducibility. In
order to show the usefulness of our algorithm, we have chosen a set of
representative numerical methods which are Simpson, Jacobi, LU factorization
and the Iterated power method.
Related papers
- Closing the Computational-Query Depth Gap in Parallel Stochastic Convex Optimization [26.36906884097317]
We develop a new parallel algorithm for minimizing Lipschitz, convex functions with a subgradient oracle.
Our result closes a gap between the best-known query depth and the best-known computational depth of parallel algorithms.
arXiv Detail & Related papers (2024-06-11T15:41:48Z) - An Efficient Algorithm for Clustered Multi-Task Compressive Sensing [60.70532293880842]
Clustered multi-task compressive sensing is a hierarchical model that solves multiple compressive sensing tasks.
The existing inference algorithm for this model is computationally expensive and does not scale well in high dimensions.
We propose a new algorithm that substantially accelerates model inference by avoiding the need to explicitly compute these covariance matrices.
arXiv Detail & Related papers (2023-09-30T15:57:14Z) - Fast Algorithms of Bath Calculations in Simulations of Quantum
System-Bath Dynamics [5.989041429080286]
We present fast algorithms for the summation of Dyson series and the inchworm Monte Carlo method for quantum systems.
The algorithms are based on evolving the integro-differential equations where the most expensive part comes from the computation of bath influence functionals.
It is proven that the proposed fast algorithms reduce the number of such calculations by a factor of $O(N)$, where $N$ is the total number of time steps.
arXiv Detail & Related papers (2022-02-13T03:00:46Z) - Using Shor's algorithm on near term Quantum computers: a reduced version [0.0]
We introduce a reduced version of Shor's algorithm that proposes a step forward in increasing the range of numbers that can be factorized on noisy Quantum devices.
In particular, we have found noteworthy results in most cases, often being able to factor the given number with only one of the proposed algorithm.
arXiv Detail & Related papers (2021-12-23T15:36:59Z) - Efficient Floating Point Arithmetic for Quantum Computers [1.189955933770711]
One of the major promises of quantum computing is the realization of SIMD (single instruction - multiple data) operations using the phenomenon of superposition.
We introduce the formalism of encoding so called semi-booleans, which allows convenient generation of unsigned integer arithmetic quantum circuits.
We extend this type of evaluation with additional features, such as ancilla-free in-place multiplication and integer coefficient evaluation.
arXiv Detail & Related papers (2021-12-20T14:00:36Z) - Fast Projected Newton-like Method for Precision Matrix Estimation under
Total Positivity [15.023842222803058]
Current algorithms are designed using the block coordinate descent method or the proximal point algorithm.
We propose a novel algorithm based on the two-metric projection method, incorporating a carefully designed search direction and variable partitioning scheme.
Experimental results on synthetic and real-world datasets demonstrate that our proposed algorithm provides a significant improvement in computational efficiency compared to the state-of-the-art methods.
arXiv Detail & Related papers (2021-12-03T14:39:10Z) - Provably Faster Algorithms for Bilevel Optimization [54.83583213812667]
Bilevel optimization has been widely applied in many important machine learning applications.
We propose two new algorithms for bilevel optimization.
We show that both algorithms achieve the complexity of $mathcalO(epsilon-1.5)$, which outperforms all existing algorithms by the order of magnitude.
arXiv Detail & Related papers (2021-06-08T21:05:30Z) - Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra [53.46106569419296]
We create classical (non-quantum) dynamic data structures supporting queries for recommender systems and least-squares regression.
We argue that the previous quantum-inspired algorithms for these problems are doing leverage or ridge-leverage score sampling in disguise.
arXiv Detail & Related papers (2020-11-09T01:13:07Z) - Refined bounds for algorithm configuration: The knife-edge of dual class
approximability [94.83809668933021]
We investigate how large should a training set be to ensure that a parameter's average metrics performance over the training set is close to its expected, future performance.
We show that if this approximation holds under the L-infinity norm, we can provide strong sample complexity bounds.
We empirically evaluate our bounds in the context of integer programming, one of the most powerful tools in computer science.
arXiv Detail & Related papers (2020-06-21T15:32:21Z) - Kernel methods through the roof: handling billions of points efficiently [94.31450736250918]
Kernel methods provide an elegant and principled approach to nonparametric learning, but so far could hardly be used in large scale problems.
Recent advances have shown the benefits of a number of algorithmic ideas, for example combining optimization, numerical linear algebra and random projections.
Here, we push these efforts further to develop and test a solver that takes full advantage of GPU hardware.
arXiv Detail & Related papers (2020-06-18T08:16:25Z) - Accelerating Feedforward Computation via Parallel Nonlinear Equation
Solving [106.63673243937492]
Feedforward computation, such as evaluating a neural network or sampling from an autoregressive model, is ubiquitous in machine learning.
We frame the task of feedforward computation as solving a system of nonlinear equations. We then propose to find the solution using a Jacobi or Gauss-Seidel fixed-point method, as well as hybrid methods of both.
Our method is guaranteed to give exactly the same values as the original feedforward computation with a reduced (or equal) number of parallelizable iterations, and hence reduced time given sufficient parallel computing power.
arXiv Detail & Related papers (2020-02-10T10:11:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.