Technology Category
Planning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsConstraint Satisfaction and Optimization: Distributed CSP/OptimizationMachine Learning: Optimization
Multiprocessor Scheduling with Memory Constraints: Fundamental Properties and Finding Optimal Solutions
🤖 AI Summary
This work addresses the joint scheduling of general-purpose computation DAGs in multi-processor systems with a two-level memory hierarchy, requiring co-optimization of load balancing, inter-processor communication overhead, and data movement under cache capacity constraints. We identify a fundamental theoretical limitation of conventional decoupled scheduling and memory management strategies: they can incur worst-case linear deviation from optimal performance, underscoring the necessity of tight compute–memory co-optimization. To this end, we propose a unified integer linear programming (ILP) framework that jointly optimizes task scheduling, data placement, and data migration decisions. Experimental evaluation on standard DAG benchmarks demonstrates that our approach consistently outperforms classical decoupled baselines, achieving significant and simultaneous improvements in both execution time and memory efficiency.
Technology Category
Application Category
📝 Abstract
We study the problem of scheduling a general computational DAG on multiple processors in a 2-level memory hierarchy. This setting is a natural generalization of several prominent models in the literature, and it simultaneously captures workload balancing, communication, and data movement due to cache size limitations. We first analyze the fundamental properties of this problem from a theoretical perspective, such as its computational complexity. We also prove that optimizing parallelization and memory management separately, as done in many applications, can result in a solution that is a linear factor away from the optimum.
On the algorithmic side, we discuss a natural technique to represent and solve the problem as an Integer Linear Program (ILP). We develop a holistic scheduling algorithm based on this approach, and we experimentally study its performance and properties on a small benchmark of computational tasks. Our results confirm that the ILP-based method can indeed find considerably better solutions than a baseline which combines classical scheduling algorithms and memory management policies.
Problem
Research questions and friction points this paper is trying to address.
Scheduling computational DAGs with memory constraints
Optimizing parallelization and memory management jointly
Finding optimal solutions using Integer Linear Programming
Innovation
Methods, ideas, or system contributions that make the work stand out.
Uses Integer Linear Program for scheduling
Holistic algorithm combines parallelization and memory
Optimizes DAG scheduling with memory constraints
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