Christodoulos Stylianou

My research focus is on developing and optimizing sparse linear algebra for efficient high-performance computing. In particular, I am interested in the optimization of Sparse Matrix Vector Multiplication (SpMV) on heterogeneous and distributed architectures through automatic format selection using AI. The selection aims to better match the underlying data structure to the computation by better exploiting the hardware characteristics and available resources, offering new optimization opportunities. I also work on performance portability for Multi- and Manycore (GPU) computing with frameworks like Kokkos and SYCL.

Morpheus

Morpheus, is a library of sparse matrix storage formats that is designed for efficient and transparent format switching across architectures without introducing prohibitive overheads at runtime and that, at the same time, enables the straightforward addition of new storage formats without major application code changes.

The library has a functional design which separates the containers that implement the storage formats from the algorithm that implement operations such as multiplication. Each container is aware of the memory space it resides in, and the algorithms require knowledge of the execution space that they will be running in. This design allows Morpheus to target multiple heterogeneous architectures.

Morpheus is a C++ header-only library written in C++17 and makes heavy use of template and meta-programming techniques. Currently supports 6 sparse matrix storage formats (COO, CSR, DIA, ELL, HYB and HDC) and 5 backends (Serial, OpenMP, CUDA, HIP and Kokkos).