- This talk will present how basic algorithmic
- blocks such as sums or dot products can be implemented in
- Julia (and have been developed
+ This talk will present how basic algorithmic blocks such as
+ sums or dot products can be implemented in Julia (and have
+ been developed
in
AccurateArithmetic.jl) in a
- way that is both accurate and efficient. This example
- illustrates how Julia can help efficiently mix
- Floating-Point-related concerns with hardware, SIMD-related
- constraints in order to get the performance of
- double-precision state-of-the-art BLAS libraries and the
- accuracy of quadruple-precision algorithms.
+ way that is both accurate and efficient. Besides naive
+ algorithms, compensated algorithms are implemented, which
+ effectively double the working precision, producing much more
+ accurate results while incurring little to no overhead,
+ especially for large input vectors. Although the vectorization
+ of such algorithms is no particularly simple task, Julia makes
+ it relatively easy and straightforward. This talk will
+ illustrate how Julia can help efficiently mix
+ Floating-Point-related concerns with SIMD-related constraints
+ in order to get the performance of double-precision
+ state-of-the-art BLAS libraries and the accuracy of
+ quadruple-precision algorithms.