Parallel execution of portfolio optimization

TL;DR

This paper explores how to efficiently parallelize portfolio optimization tasks using multi-core PCs and clusters, aiming to reduce computational complexity in asset liability management for financial institutions.

Contribution

It analyzes the parallel organization of portfolio optimization, evaluates cluster-based solutions, and identifies the most efficient cluster architecture for these computationally intensive tasks.

Findings

Cluster computing improves optimization efficiency

Multi-core PCs are a cost-effective solution

Optimal cluster architecture enhances performance

Abstract

Analysis of asset liability management (ALM) strategies especially for long term horizon is a crucial issue for banks, funds and insurance companies. Modern economic models, investment strategies and optimization criteria make ALM studies computationally very intensive task. It attracts attention to multiprocessor system and especially to the cheapest one: multi core PCs and PC clusters. In this article we are analyzing problem of parallel organization of portfolio optimization, results of using clusters for optimization and the most efficient cluster architecture for these kinds of tasks.