Calculate with error-free, faithful, and compensated transforms and extended signficands.
Author JuliaMath
23 Stars
Updated Last
11 Months Ago
Started In
January 2018


Floating point math with error-free, faithful, and compensated transforms.

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Error-free and faithful transforms

AccurateArithmetic.jl provides a set of error-free transforms (EFTs), which allow getting not only the rounded result of a floating-point computation, but also the accompanying rounding error:

julia> using AccurateArithmetic

# WARNING: a is not really 1/10, as this value is not representable as a Float64
# (and similarly for b)
julia> (a, b) = (0.1, 0.2)

julia> (s, e) = AccurateArithmetic.two_sum(a, b)
(0.30000000000000004, -2.7755575615628914e-17)

In the above example, s is the result of the floating-point addition 0.1+0.2, rounded to the nearest representable floating-point number, exactly what you would get from a standard addition. e is the rounding error associated to s. In other words, it is guaranteed that a + b = s + e, in a strict mathematical sense (i.e. when the + operate on real numbers and are not rounded).

Similar EFTs are provided for the binary subtraction (two_diff) and multiplication (two_prod). Some operations of higher arity are also supported, such as three_sum, four_sum or three_prod.

Compensated algorithms

EFTs can be leveraged to build "compensated algorithms", which compute a result as if the basic algorithm had been run using a higher precision.

# By construction, this vector sums to 1
julia> x = 5000 |> N->randn(N) .* exp.(10 .* randn(N)) |> x->[x;-x;1.0] |> x->x[sortperm(rand(length(x)))];
julia> sum(big.(x))

# But the standard summation algorithms computes this sum very inaccurately
# (not even the sign is correct)
julia> sum(x)

# Compensated summation algorithms should compute this more accurately
julia> using AccurateArithmetic

# Algorithm by Ogita, Rump and Oishi
julia> sum_oro(x)

# Algorithm by Kahan, Babuska and Neumaier
julia> sum_kbn(x)

In the graph above, we see the relative error vary as a function of the condition number, in a log-log scale. Errors lower than ϵ are arbitrarily set to ϵ; conversely, when the relative error is more than 100% (i.e no digit is correctly computed anymore), the error is capped there in order to avoid affecting the scale of the graph too much. What we see is that the pairwise summation algorithm (as implemented in Base.sum) starts losing accuracy as soon as the condition number increases, computing only noise when the condition number exceeds 1/ϵ≃10¹⁶. In contrast, both compensated algorithms (Kahan-Babuska-Neumaier and Ogita-Rump-Oishi) still accurately compute the result at this point, and start losing accuracy there, computing meaningless results when the condition nuber reaches 1/ϵ²≃10³². In effect these (simply) compensated algorithms produce the same results as if a naive summation had been performed with twice the working precision (128 bits in this case), and then rounded to 64-bit floats.

Performancewise, compensated algorithms perform a lot better than alternatives such as arbitrary precision (BigFloat) or rational arithmetic (Rational) :

julia> using BenchmarkTools

julia> @btime sum($x)
  1.305 μs (0 allocations: 0 bytes)

julia> @btime sum_oro($x)
  3.421 μs (0 allocations: 0 bytes)

julia> @btime sum_kbn($x)
  3.792 μs (0 allocations: 0 bytes)

julia> @btime sum(big.($x))
  874.203 μs (20006 allocations: 1.14 MiB)

julia> @btime sum(Rational{BigInt}.(x))
  22.702 ms (582591 allocations: 10.87 MiB)

However, compensated algorithms perform a larger number of elementary operations than their naive floating-point counterparts. As such, they usually perform worse. However, leveraging the power of modern architectures via vectorization, the slow down can be kept to a small value.

In the graph above, the time spent in the summation (renormalized per element) is plotted against the vector size (the units in the y-axis label should be “ns/elem”). What we see with the standard summation is that, once vectors start having significant sizes (say, more than 1000 elements), the implementation is memory bound (as expected of a typical BLAS1 operation). Which is why we see significant decreases in the performance when the vector can’t fit into the L2 cache (around 30k elements, or 256kB on my machine) or the L3 cache (around 400k elements, or 3MB on y machine).

The Ogita-Rump-Oishi algorithm, when implemented with a suitable unrolling level (ushift=2, i.e 2²=4 unrolled iterations), is CPU-bound when vectors fit inside the cache. However, when vectors are to large to fit into the L3 cache, the implementation becomes memory-bound again (on my system), which means we get the same performance as the standard summation.

In other words, the improved accuracy is free for sufficiently large vectors. For smaller vectors, the accuracy comes with a slow-down that can reach values slightly above 3 for vectors which fit in the L2 cache.


The graphs above can be reproduced using the test/perftests.jl script in this repository. Before running them, be aware that it takes around one hour to generate the performance graph, during which the benchmark machine should be as low-loaded as possible in order to avoid perturbing performance measurements.


  • C. Elrod and F. Févotte, "Accurate and Efficient Sums and Dot Products in Julia". preprint

  • T. Ogita, S. Rump and S. Oishi, "Accurate sum and dot product", SIAM Journal on Scientific Computing, 6(26), 2005. DOI: 10.1137/030601818

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