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A New Approach to Probabilistic Rounding Error Analysis

by Nick Higham and Theo Mary

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Backward error for random linear system of dimension n.

James Wilkinson developed a systematic way of analyzing rounding errors in numerical algorithms in the 1960s—a time when computers typically used double precision arithmetic and a matrix of dimension n = 100 was considered large. As a result, an error bound with a dominant term p(n)u, for p a low degree polynomial and u the machine precision, was acceptably small.

Today, the supercomputers heading the TOP500 list solve linear systems of equations of dimension 10^8 and half precision arithmetic (accurate to about 4 significant decimal digits) is increasingly available in hardware, notably on graphical processing units (GPUs) from AMD and NVIDIA. Traditional rounding error bounds cannot guarantee accurate results when the dimension is very large or the precision is low. Yet computations are often successful in such cases—for example in machine learning algorithms making use of half, or even lower, precision.

This discrepancy between theory and practice stems from the fact that traditional rounding error bounds are worst-case bounds and so tend to be pessimistic. Indeed, while a single floating-point operation incurs a rounding error bounded in modulus by u, the composition of n operations leads to a worst-case error bound with dominant term proportional to nu. But this worst-case error bound is attained only when each rounding error is of maximal magnitude and identical sign, which is very unlikely. Since the beginning of the digital computer era many researchers have modelled rounding errors as random variables in an attempt to obtain better estimates of how the error behaves on average. This line of thinking has led to the well-known rule of thumb, based on informal arguments and assumptions, that constants in rounding error bounds can be replaced by their square roots.

In our EPrint A New Approach to Probabilistic Rounding Error Analysis we make minimal probabilistic assumptions on the rounding errors and make use of a tool from probability theory called a concentration inequality. We show that in several core linear algebra algorithms, including matrix-vector products and LU factorization, the backward error can be bounded with high probability by a relaxed constant proportional to \sqrt{n\log n}u instead of nu. Our analysis provides the first rigorous justification of the rule of thumb.

This new bound is illustrated in the figure above, where we consider the solution of a linear system Ax = b by LU factorization. The matrix A and vector x have entries from the random uniform [0,1] distribution and b is formed as Ax. We compare the backward error with its traditional worst-case bound and our relaxed probabilistic bound. The figure shows that the probabilistic bound is in very good agreement with the actual backward error and is much smaller than the traditional bound. Moreover, it successfully captures the asymptotic behavior of the error growth, which follows \sqrt{n} rather than n.

The assumptions underlying our analysis—that the rounding errors are independent random variables of mean zero—do not always hold, as we illustrate with examples in the paper. Nevertheless, our experiments show that the bounds do correctly predict the backward error for a selection of real-life matrices from the SuiteSparse collection.

Fast Solution of Linear Systems via GPU Tensor Cores’ FP16 Arithmetic and Iterative Refinement

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NVIDIA Founder & CEO Jensen Huang talking about the work reported here in his special address at Supercomputing 2018 (8:30 onwards).

Over the last 30 years, hierarchical computer memories, multicore processors and graphical processing units (GPUs) have all necessitated the redesign of numerical linear algebra algorithms, and in doing so have led to algorithmic innovations. Mixed precision arithmetic—a concept going back to the earliest computers, which had the ability to accumulate inner products in extra precision—attracted renewed interest in the late 1990s once Intel chips were able to execute single precision at twice the rate of double precision. Now the increasing availability of low precision arithmetic is offering new opportunities.

In the paper Harnessing GPU Tensor Cores for Fast FP16 Arithmetic to Speed up Mixed-Precision Iterative Refinement Solvers presented at SC18 (the leading supercomputing conference), Azzam Haidar, Stanimire Tomov, Jack Dongarra and Nick Higham show how to exploit the half precision (fp16) arithmetic that is now available in hardware. Whereas fp16 arithmetic can be expected to run at twice the rate of fp32 (single precision) arithmetic, the NVIDIA V100 GPU has tensor cores that can execute half precision at up to eight times the speed of single precision and can deliver the results to single precision accuracy. Developing algorithms that can exploit half precision arithmetic is important both for a workstation connected to a single V100 GPU and the world’s fastest computer (as of November 2018): Summit at Oak Ridge National Laboratory, which contains 27,648 V100 GPUs.

The paper shows that a dense n-by-n double precision linear system Ax = b can be solved using mixed precision iterative refinement at a rate up to four times faster than a highly optimized double precision solver and with a reduction in energy consumption by a factor five.

The key idea is to LU factorize the matrix A in a mix of half precision and single precision then apply iterative refinement. The update equations in the refinement process are solved by an inner GMRES iteration that uses the LU factors as preconditioners. This GMRES-IR algorithm was proposed by Carson and Higham in two (open access) papers in SIAM J. Sci. Comput. (2017 and 2018). In the form used here, the algorithm converges for matrices with condition numbers up to about 10^8. It provides a backward stable, double precision solution while carrying out almost all the flops at lower precision.

Codes implementing this work will be released through the open-source MAGMA library.

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Primary Solutions of Matrix Equations

Max Fasi and Bruno Iannazzo have shown how to compute all primary solutions of a matrix equation f(X) = A for rational functions f. A primary solution is one that can be written as a polynomial in A. The proposed algorithm exploits the Schur decomposition and generalizes earlier algorithms for matrix roots.

Fasi and Iannazzo’s paper Computing Primary Solutions of Equations Involving Primary Matrix Functions appears in Linear Algebra Appl. 560, 17-42, 2019.

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