Exascale machines require new programming paradigms and runtimes

Extreme scale parallel computing systems will have tens of thousands of optionally accelerator-equiped nodes with hundreds of cores each, as well as deep memory hierarchies and complex interconnect topologies. Such Exascale systems will provide hardware parallelism at multiple levels and will be energy constrained. Their extreme scale and the rapidly deteriorating reliablity of their hardware components means that Exascale systems will exhibit low mean-time-between-failure values. Furthermore, existing programming models already require heroic programming and optimisation efforts to achieve high efficiency on current supercomputers. Invariably, these efforts are platform-specific and non-portable. In this paper we will explore the shortcomings of existing programming models and runtime systems for large scale computing systems. We then propose and discuss important features of programming paradigms and runtime system to deal with large scale computing systems with a special focus on data-intensive applications and resilience. Finally, we also discuss code sustainability issues and propose several software metrics that are of paramount importance for code development for large scale computing systems

Scheduling and Resource Allocation

International audienceChap.8 SCHEDULING AND RESOURCE ALLOCATION - 8.1 Introduction: Energy-Aware Scheduling 8.2 Use of Linear Programming in Energy-Aware Scheduling 8.2.1 Finding the Optimal Solution Using a Linear Program 8.2.2 Benefits and Limitations of LP 8.3 Heuristics in Large Instances 8.3.1 Energy-Aware Greedy Algorithms 8.3.2 Vector Packing 8.3.3 Improving Fast Algorithms 8.4 Comparing Allocation Heuristics for Energy-Aware Scheduling 8.4.1 Problem Formulation 8.4.2 Allocation Heuristics 8.4.3 Results 8.5 Energy-Aware Task Allocation in Mobile Environments 8.5.1 Reference Architecture 8.5.2 Task Allocation Strategy 8.5.3 Task Allocation Algorithm 8.5.4 Performance Results 8.6 An Energy-Aware Scheduling Strategy for Allocating Computational Tasks in a Fully Decentralized Way 8.6.1 Decentralized Resources in Cloud: Overview 8.6.2 Cooperative Scheduling Anti-Load Balancing Algorithm for Cloud (CSAAC) 8.6.3 Simulation Results 8.6.4 Evaluation 8.7 Cost-Aware Scheduling with Smart Grids 8.7.1 Cost-Aware Scheduling 8.7.2 Cost-Aware Scheduling Using DE 8.7.3 Comparison of DE with Other Approaches 8.8 Heterogeneity, Cooling, DVFS, and Migration 8.8.1 Lever Interactions 8.8.2 Infrastructures 8.8.3 Resource Allocation as a Whole 8.9 Conclusions - Reference