BAG : Managing GPU as buffer cache in operating systems

This paper presents the design, implementation and evaluation of BAG, a system that manages GPU as the buffer cache in operating systems. Unlike previous uses of GPUs, which have focused on the computational capabilities of GPUs, BAG is designed to explore a new dimension in managing GPUs in heterogeneous systems where the GPU memory is an exploitable but always ignored resource. With the carefully designed data structures and algorithms, such as concurrent hashtable, log-structured data store for the management of GPU memory, and highly-parallel GPU kernels for garbage collection, BAG achieves good performance under various workloads. In addition, leveraging the existing abstraction of the operating system not only makes the implementation of BAG non-intrusive, but also facilitates the system deployment

Optimality of the Fastest Available Server Policy

We give sufficient conditions under which a policy that assigns customers to the Fastest Available Server, labelled FAS, is optimal in queueing models with multiple independent Poisson arrival processes and heterogeneous parallel exponential servers. The criterion is to minimize the long-run average cost per unit time. We obtain results for loss models and for queueing systems with a finite-capacity or infinite-capacity buffer under a head-of-the-line priority scheme. The results depend on cost assumptions, so we analyze the robustness of the cost structure and present counter-examples to illustrate when FAS is not optimal

Task scheduling techniques for asymmetric multi-core systems

As performance and energy efficiency have become the main challenges for next-generation high-performance computing, asymmetric multi-core architectures can provide solutions to tackle these issues. Parallel programming models need to be able to suit the needs of such systems and keep on increasing the application’s portability and efficiency. This paper proposes two task scheduling approaches that target asymmetric systems. These dynamic scheduling policies reduce total execution time either by detecting the longest or the critical path of the dynamic task dependency graph of the application, or by finding the earliest executor of a task. They use dynamic scheduling and information discoverable during execution, fact that makes them implementable and functional without the need of off-line profiling. In our evaluation we compare these scheduling approaches with two existing state-of the art heterogeneous schedulers and we track their improvement over a FIFO baseline scheduler. We show that the heterogeneous schedulers improve the baseline by up to 1.45 in a real 8-core asymmetric system and up to 2.1 in a simulated 32-core asymmetric chip.This work has been supported by the Spanish Government (SEV2015-0493), by the Spanish Ministry of Science and Innovation (contract TIN2015-65316-P), by Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272), by the RoMoL ERC Advanced Grant (GA 321253) and the European HiPEAC Network of Excellence. The Mont-Blanc project receives funding from the EU’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no 610402 and from the EU’s H2020 Framework Programme (H2020/2014-2020) under grant agreement no 671697. M. Moretó has been partially supported by the Ministry of Economy and Competitiveness under Juan de la Cierva postdoctoral fellowship number JCI-2012-15047. M. Casas is supported by the Secretary for Universities and Research of the Ministry of Economy and Knowledge of the Government of Catalonia and the Cofund programme of the Marie Curie Actions of the 7th R&D Framework Programme of the European Union (Contract 2013 BP B 00243).Peer ReviewedPostprint (author's final draft

A Design of a Generic Profile-Based Queue System

Website and server hosting accounts impose resource limits which restrict the processing power available to applications. One technique to bypass these restrictions is to split up large jobs into smaller tasks that can then be queued and processed task by task. This is a fairly common need. However, different application jobs can differ widely in nature and in their requirements. Thus, a queue system built for one job type may not be entirely suitable for another. This situation could result in the having to implement separate, additional queue systems for different needs. This research proposes a generic queue core design that can accommodate a large variety of job types by providing a basic set of features which can be easily extended to add specificity. The design includes a detailed discussion on queue implementation, scheduling, directory structure and business tier logic. Furthermore, it features highly configurable, time-sensitive performance management that can be customized for any job type. This is provided as the ability to indicate desired performance profiles for any given slot of time during the week. Actual performance data based on the usage of a prototype is also included to demonstrate the significant advantage of using the queue system