Many important applications including machine learning, molecular dynamics, and computational fluid dynamics, use sparse data. Processing sparse data leads to non-affine loop bounds and frustrates the use of the polyhedral model for code transformation. The Sparse Polyhedral Framework (SPF) addresses limitations of the Polyhedral model by supporting non-affine constraints in sets and relations using uninterpreted functions. This work contributes an object-oriented API that wraps the SPF intermediate representation (IR) and integrates the Inspector/Executor Generation Library and Omega+ for precise set and relation manipulation and code generation. The result is a well-specified definition of a full computation using the SPF IR. The API prov...
International audienceThe polyhedral model is a powerful framework for automatic optimization and pa...
Many advances in automatic parallelization and optimization have been achieved through the polyhedra...
This research proposes an intermediate compiler representation designed for optimization, with an em...
Many important applications including machine learning, molecular dynamics, and computational fluid ...
Irregular applications such as big graph analysis, material simulations, molecular dynamics simulati...
Scientific applications are computationally intensive and require expensive HPC resources. Optimizin...
Applications that manipulate sparse data structures contain memory reference patterns that are unkno...
Applications that manipulate sparse data structures contain memory reference patterns that are un-kn...
Scientific applications, especially legacy applications, contain a wealth of scientific knowledge. A...
The Polyhedral Model is one of the most powerful framework for automatic optimization and paralleliz...
Polyhedral compilation is widely used in high-level synthesis tools and in production compilers such...
We describe a novel approach to sparse {\em and} dense SPMD code generation: we view arrays (sparse ...
We describe a novel approach to sparse and dense SPMD code generation: we view arrays (sparse and d...
Abstract. The polyhedral model is a powerful framework for automatic optimization and parallelizatio...
International audienceThe polyhedral model is a powerful framework for automatic optimization and pa...
Many advances in automatic parallelization and optimization have been achieved through the polyhedra...
This research proposes an intermediate compiler representation designed for optimization, with an em...
Many important applications including machine learning, molecular dynamics, and computational fluid ...
Irregular applications such as big graph analysis, material simulations, molecular dynamics simulati...
Scientific applications are computationally intensive and require expensive HPC resources. Optimizin...
Applications that manipulate sparse data structures contain memory reference patterns that are unkno...
Applications that manipulate sparse data structures contain memory reference patterns that are un-kn...
Scientific applications, especially legacy applications, contain a wealth of scientific knowledge. A...
The Polyhedral Model is one of the most powerful framework for automatic optimization and paralleliz...
Polyhedral compilation is widely used in high-level synthesis tools and in production compilers such...
We describe a novel approach to sparse {\em and} dense SPMD code generation: we view arrays (sparse ...
We describe a novel approach to sparse and dense SPMD code generation: we view arrays (sparse and d...
Abstract. The polyhedral model is a powerful framework for automatic optimization and parallelizatio...
International audienceThe polyhedral model is a powerful framework for automatic optimization and pa...
Many advances in automatic parallelization and optimization have been achieved through the polyhedra...
This research proposes an intermediate compiler representation designed for optimization, with an em...