Loop pipelining is one of the most important optimization methods in high-level synthesis
(HLS) for increasing loop parallelism. There has been considerable work on improving loop …

Irregular data structures, as exemplified with sparse matrices, have proved to be essential in
modern computing. Numerous sparse formats have been investigated to improve the overall …

In this paper, we present a new runtime code generation technique for speculative loop
optimization and parallelization. The main benefit of this technique, compared to previous …

Sparse linear algebra is central to many scientific programs, yet compilers fail to optimize it
well. High-performance libraries are available, but adoption costs are significant. Moreover …

A few weeks ago, we were glad to announce the first release of Apollo, the Automatic
speculative POLyhedral Loop Opti-mizer. Apollo applies polyhedral optimizations on-the-fly …

In this paper, we propose a runtime framework that implements code multi-versioning and
specialization to optimize and parallelize loop kernels that are invoked many times with …

Data-dependence profiling is a program-analysis technique for detecting parallelism
opportunities in sequential programs. It captures data dependences that actually occur …

The last improvements in programming languages and models have focused on simplicity
and abstraction; leading Python to the top of the list of the programming languages …

A formal, high-level representation of programs is typically needed for static and dynamic
analyses performed by compilers. However, the source code of target applications is not …

Leveraging the SIMD capability of modern CPU architectures is mandatory to take full
advantage of their increased performance. To exploit this capability, binary executables …