# An Adaptive Performance-oriented Scheduler for Static and Dynamic Heterogeneity

## TL;DR

This paper introduces a dynamic, adaptive scheduler using a Performance Trace Table (PTT) within the XiTAO framework to optimize task scheduling on heterogeneous hardware, improving performance and responsiveness.

## Contribution

It presents a novel PTT mechanism for adaptive scheduling in XiTAO, enabling better handling of heterogeneity and interference in both static and dynamic environments.

## Key findings

- Up to 3.25x speedup on NVIDIA Jetson TX2 with DAG benchmarks.
- Effective adaptation to interference from background processes.
- Successful porting of VGG-16 CNN framework demonstrating practical applicability.

## Abstract

With the emergence of heterogeneous hardware paving the way for the post-Moore era, it is of high importance to adapt the runtime scheduling to the platform's heterogeneity. To enhance adaptive and responsive scheduling, we introduce a Performance Trace Table (PTT) into XiTAO, a framework for elastic scheduling of mixed-mode parallelism. The PTT is an extensible and dynamic lightweight manifest of the per-core latency that can be used to guide the scheduling of both critical and non-critical tasks. By understanding the per-task latency, the PTT can infer task performance, intra-application interference as well as inter-application interference. We run random Direct Acyclic Graphs (DAGs) of different workload categories as a benchmark on NVIDIA Jetson TX2 chip, achieving up to 3.25x speedup over a standard work-stealing scheduler. To exemplify scheduling adaption to interference, we run DAGs with high parallelism and analyze the scheduler's response to interference from a background process on an Intel Haswell (2650v3) multicore workstation. We also showcase the XiTAO's scheduling performance by porting the VGG-16 image classification framework based on Convolutional Neural Networks (CNN).