Introduction

The field of compute resource allocation and optimization is rapidly evolving, driven by the increasing demand for efficient and scalable computing systems. Recent developments have focused on designing innovative solutions to optimize resource utilization, reduce costs, and improve performance.

General Direction

The field is moving towards the adoption of market-based solutions, adaptive orchestration, and performance modeling to optimize compute resource allocation. These approaches enable dynamic allocation of resources, taking into account factors such as cost, performance, and resilience. Additionally, there is a growing emphasis on developing solutions that can efficiently harness heterogeneous accelerators, such as GPUs and specialized accelerators, to accelerate compute-intensive workloads.

Noteworthy Papers

• A market-based solution for provisioning compute resources across planet-wide clusters has been proposed, which demonstrates an efficient transition of users from congested to less congested resources.
• Maya, a performance modeling system, has been developed to optimize deep learning training workloads using emulated virtual accelerators, achieving less than 5% prediction error across diverse models and optimization strategies.