# A Multilevel Approach for the Performance Analysis of Parallel Algorithms

## TL;DR

This paper introduces a multilevel framework for analyzing parallel algorithm performance by decomposing algorithms into operators and matrices to reveal parallelism and overheads.

## Contribution

It presents a novel multilevel method that models parallel algorithms through operator sets and matrix representations to better understand their performance characteristics.

## Key findings

- Decomposition level influences algorithm granularity.
- Block matrices reveal inherent parallelism.
- Analysis identifies sources of overheads.

## Abstract

We provide a multilevel approach for analysing performances of parallel algorithms. The main outcome of such approach is that the algorithm is described by using a set of operators which are related to each other according to the problem decomposition. Decomposition level determines the granularity of the algorithm. A set of block matrices (decomposition and execution) highlights fundamental characteristics of the algorithm, such as inherent parallelism and sources of overheads.