Consolidating the innovative concepts towards Exascale computing for Co-Design of Co-Applications ll: Co-Design Automation - Workload Characterization

TL;DR

This paper proposes a co-design automation framework for many-core architectures tailored for co-application execution, emphasizing customizable workload generation and simulation-based validation to reduce design complexity and improve performance.

Contribution

It introduces a novel co-design automation process with a customizable workload generator, addressing current benchmark limitations for many-core architecture evaluation.

Findings

Automated co-design process effectively reduces development time.

Customizable workload model generates complex, application-specific benchmarks.

Simulation results validate the proposed co-design approach.

Abstract

Many-core co-design is a complex task in which application complexity design space, heterogeneous many-core architecture design space, parallel programming language design space, simulator design space and optimizer design space should get integrated through a binding process and these design spaces, an ensemble of what is called many-core co-design spaces. It is indispensable to build a co-design automation process to dominate over the co-design complexity to cut down the turnaround time. The co-design automation is frame worked to comprehend the dependencies across the many-core co-design spaces and devise the logic behind these interdependencies using a set of algorithms. The software modules of these algorithms and the rest from the many-core co-design spaces interact to crop up the power-performance optimized heterogeneous many-core architecture specific for the simultaneous execution of co applications without space-time sharing. It is essential that such co-design automation has a built-in user-customizable workload generator to benchmark the emerging many-core architecture. This customizability benefits the generation of complex workloads with the desired computation complexity, communication complexity, control flow complexity, and locality of reference, specified under a distribution and established on quantitative models. In addition, the customizable workload model aids the generation of what is called computational and communication surges. None of the current day benchmark suites encompasses applications and kernels that can match the attributes of customizable workload model proposed in this paper. Aforementioned concepts are exemplified in, the case study supported by simulation results gathered from the simulator.