Performance Models for Data Transfers: A Case Study with Molecular Chemistry Kernels

With increasing complexity of hardwares, systems with different memory nodes are ubiquitous in High Performance Computing (HPC). It is paramount to develop strategies to overlap the data transfers between memory nodes with computations in order to exploit the full potential of these systems. In this article, we consider the problem of deciding the order of data transfers between two memory nodes for a set of independent tasks with the objective to minimize the makespan. We prove that with limited memory capacity, obtaining the optimal order of data transfers is a NP-complete problem. We propose several heuristics for this problem and provide details about their favorable situations. We present an analysis of our heuristics on traces, obtained by running 2 molecular chemistry kernels, namely, Hartree-Fock (HF) and Coupled Cluster Single Double (CCSD) on 10 nodes of an HPC system. Our results show that some of our heuristics achieve significant overlap for moderate memory capacities and are very close to the lower bound of makespan

A hyper-heuristic for adaptive scheduling in computational grids

In this paper we present the design and implementation of an hyper-heuristic for efficiently scheduling independent jobs in computational grids. An efficient scheduling of jobs to grid resources depends on many parameters, among others, the characteristics of the resources and jobs (such as computing capacity, consistency of computing, workload, etc.). Moreover, these characteristics change over time due to the dynamic nature of grid environment, therefore the planning of jobs to resources should be adaptively done. Existing ad hoc scheduling methods (batch and immediate mode) have shown their efficacy for certain types of resource and job characteristics. However, as stand alone methods, they are not able to produce the best planning of jobs to resources for different types of Grid resources and job characteristics. In this work we have designed and implemented a hyper-heuristic that uses a set of ad hoc (immediate and batch mode) scheduling methods to provide the scheduling of jobs to Grid resources according to the Grid and job characteristics. The hyper-heuristic is a high level algorithm, which examines the state and characteristics of the Grid system (jobs and resources), and selects and applies the ad hoc method that yields the best planning of jobs. The resulting hyper-heuristic based scheduler can be thus used to develop network-aware applications that need efficient planning of jobs to resources. The hyper-heuristic has been tested and evaluated in a dynamic setting through a prototype of a Grid simulator. The experimental evaluation showed the usefulness of the hyper-heuristic for planning of jobs to resources as compared to planning without knowledge of the resource and job characteristics.Peer ReviewedPostprint (author's final draft

Learning Scheduling Algorithms for Data Processing Clusters

Efficiently scheduling data processing jobs on distributed compute clusters requires complex algorithms. Current systems, however, use simple generalized heuristics and ignore workload characteristics, since developing and tuning a scheduling policy for each workload is infeasible. In this paper, we show that modern machine learning techniques can generate highly-efficient policies automatically. Decima uses reinforcement learning (RL) and neural networks to learn workload-specific scheduling algorithms without any human instruction beyond a high-level objective such as minimizing average job completion time. Off-the-shelf RL techniques, however, cannot handle the complexity and scale of the scheduling problem. To build Decima, we had to develop new representations for jobs' dependency graphs, design scalable RL models, and invent RL training methods for dealing with continuous stochastic job arrivals. Our prototype integration with Spark on a 25-node cluster shows that Decima improves the average job completion time over hand-tuned scheduling heuristics by at least 21%, achieving up to 2x improvement during periods of high cluster load

A C-DAG task model for scheduling complex real-time tasks on heterogeneous platforms: preemption matters

Recent commercial hardware platforms for embedded real-time systems feature heterogeneous processing units and computing accelerators on the same System-on-Chip. When designing complex real-time application for such architectures, the designer needs to make a number of difficult choices: on which processor should a certain task be implemented? Should a component be implemented in parallel or sequentially? These choices may have a great impact on feasibility, as the difference in the processor internal architectures impact on the tasks' execution time and preemption cost. To help the designer explore the wide space of design choices and tune the scheduling parameters, in this paper we propose a novel real-time application model, called C-DAG, specifically conceived for heterogeneous platforms. A C-DAG allows to specify alternative implementations of the same component of an application for different processing engines to be selected off-line, as well as conditional branches to model if-then-else statements to be selected at run-time. We also propose a schedulability analysis for the C-DAG model and a heuristic allocation algorithm so that all deadlines are respected. Our analysis takes into account the cost of preempting a task, which can be non-negligible on certain processors. We demonstrate the effectiveness of our approach on a large set of synthetic experiments by comparing with state of the art algorithms in the literature