Mashing load balancing algorithm to boost hybrid kernels in molecular dynamics simulations

The path to the efficient exploitation of molecular dynamics simulators is strongly driven by the increasingly intensive use of accelerators. However, they suffer performance portability issues, making it necessary both to achieve technological combinations that allow taking advantage of each programming model and device, and to define more effective load distribution strategies that consider the simulation conditions. In this work, a new load balancing algorithm is presented, together with a set of optimizations to support hybrid co-execution in a runtime system for heterogeneous computing. The new extended design enables the exploitation of custom kernels and acceleration technologies altogether, being encapsulated for the rest of the runtime and its scheduling system. With this support, Mash algorithm allows to simultaneously leverage different workload distribution strategies, benefiting from the most advantageous one per device and technology. Experiments show that these proposals achieve an efficiency close to 0.90 and an energy efficiency improvement around 1.80 over the original optimized version.This work has been supported by the Spanish Ministry of Education (FPU16/03299 grant), the Spanish Science and Technology Commission under contract PID2019-105660RB-C22 and performed under the Project HPC-EUROPA3 (INFRAIA-2016-1-730897), with the support of the EC Research Innovation Action (H2020). The author gratefully acknowledges the support of the SPMT group, part of HLRS

Improving utilization of heterogeneous clusters

Datacenters often agglutinate sets of nodes with different capabilities, leading to a sub-optimal resource utilization. One of the best ways of improving utilization is to balance the load by taking into account the heterogeneity of these clusters. This article presents a novel way of expressing computational capacity, more adequate for heterogeneous clusters, and also advocates for task migration in order to further improve the utilization. The experimental evaluation shows that both proposals are advantageous and allow improving the utilization of heterogeneous clusters and reducing the makespan to 16.7% and 17.1%, respectively.This work has been supported by the Spanish Science and Technology Commission under contracts TIN2016-76635-C2-2-R and TIN2016-81840-REDT (CAPAP-H6 network) and the European HiPEAC Network of Excellenc

Sigmoid: An auto-tuned load balancing algorithm for heterogeneous systems

A challenge that heterogeneous system programmers face is leveraging the performance of all the devices that integrate the system. This paper presents Sigmoid, a new load balancing algorithm that efficiently co-executes a single OpenCL data-parallel kernel on all the devices of heterogeneous systems. Sigmoid splits the workload proportionally to the capabilities of the devices, drastically reducing response time and energy consumption. It is designed around several features; it is dynamic, adaptive, guided and effortless, as it does not require the user to give any parameter, adapting to the behaviourof each kernel at runtime. To evaluate Sigmoid's performance, it has been implemented in Maat, a system abstraction library. Experimental results with different kernel types show that Sigmoid exhibits excellent performance, reaching a utilization of 90%, together with energy savings up to 20%, always reducing programming effort compared to OpenCL, and facilitating the portability to other heterogeneous machines.This work has been supported by the Spanish Science and Technology Commission under contract PID2019-105660RB-C22 and the European HiPEAC Network of Excellence

Characterizing the Communication Demands of the Graph500 Benchmark on a Commodity Cluster

Big Data applications have gained importance over the last few years. Such applications focus on the analysis of huge amounts of unstructured information and present a series of differences with traditional High Performance Computing (HPC) applications. For illustrating such dissimilarities, this paper analyzes the behavior of the most scalable version of the Graph500 benchmark when run on a state-of-the-art commodity cluster facility. Our work shows that this new computation paradigm stresses the interconnection subsystem. In this work, we provide both analytical and empirical characterizations of the Graph500 benchmark, showing that its communication needs bound the achieved performance on a cluster facility. Up to our knowledge, our evaluation is the first to consider the impact of message aggregation on the communication overhead and explore a tradeoff that diminishes benchmark execution time, increasing system performance

A clustering-based knowledge discovery process for data centre infrastructure management

Data centre infrastructure management (DCIM) is the integration of information technology and facility management disciplines to centralise monitoring and management in data centres. One of the most important problems of DCIM tools is the analysis of the huge amount of data obtained from the real-time monitoring of thousands of resources. In this paper, an adaptation of the knowledge discovery process for dealing with the data analysis in DCIM tools is proposed. A case of study based on monitoring and labelling of nodes of a high performance computing data centre in real time is presented. This shows that characterising the state of the nodes according to a reduced and relevant set of metrics is feasible and its outcome directly usable, simplifying consequently the decision-making process in these complex infrastructures. © 2016, Springer Science+Business Media New York.This work has been supported by the Spanish Science and Technol- ogy Commission (CICYT) under contract TIN2013-46957-C2-2-P and CAPAP-H5 network TIN2014-53522, the European HiPEAC Network of Excellence

Auto-tuned OpenCL kernel co-execution in OmpSs for heterogeneous systems

The emergence of heterogeneous systems has been very notable recently. The nodes of the most powerful computers integrate several compute accelerators, like GPUs. Profiting from such node configurations is not a trivial endeavour. OmpSs is a framework for task based parallel applications, that allows the execution of OpenCl kernels on different compute devices. However, it does not support the co-execution of a single kernel on several devices. This paper presents an extension of OmpSs that rises to this challenge, and presents Auto-Tune, a load balancing algorithm that automatically adjusts its internal parameters to suit the hardware capabilities and application behavior. The extension allows programmers to take full advantage of the computing devices with negligible impact on the code. It takes care of two main issues. First, the automatic distribution of datasets and the management of device memory address spaces. Second, the implementation of a set of load balancing algorithms to adapt to the particularities of applications and systems. Experimental results reveal that the co-execution of single kernels on all the devices in the node is beneficial in terms of performance and energy consumption, and that Auto-Tune gives the best overall results.This work has been supported by the University of Cantabria with grant CVE-2014-18166, the Generalitat de Catalunya under grant 2014-SGR-1051, the Spanish Ministry of Economy, Industry and Competitiveness under contracts TIN2016-76635-C2-2-R (AEI/FEDER, UE) and TIN2015-65316-P. The Spanish Government through the Programa Severo Ochoa (SEV-2015-0493

Assessing the Suitability of King Topologies for Interconnection Networks

In the late years many different interconnection networks have been used with two main tendencies. One is characterized by the use of high-degree routers with long wires while the other uses routers of much smaller degree. The latter rely on two-dimensional mesh and torus topologies with shorter local links. This paper focuses on doubling the degree of common 2D meshes and tori while still preserving an attractive layout for VLSI design. By adding a set of diagonal links in one direction, diagonal networks are obtained. By adding a second set of links, networks of degree eight are built, named king networks. This research presents a comprehensive study of these networks which includes a topological analysis, the proposal of appropriate routing procedures and an empirical evaluation. King networks exhibit a number of attractive characteristics which translate to reduced execution times of parallel applications. For example, the execution times NPB suite are reduced up to a 30 percent. In addition, this work reveals other properties of king networks such as perfect partitioning that deserves further attention for its convenient exploitation in forthcoming high-performance parallel systems

CATA: Criticality aware task acceleration for multicore processors

Managing criticality in task-based programming models opens a wide range of performance and power optimization opportunities in future manycore systems. Criticality aware task schedulers can benefit from these opportunities by scheduling tasks to the most appropriate cores. However, these schedulers may suffer from priority inversion and static binding problems that limit their expected improvements. Based on the observation that task criticality information can be exploited to drive hardware reconfigurations, we propose a Criticality Aware Task Acceleration (CATA) mechanism that dynamically adapts the computational power of a task depending on its criticality. As a result, CATA achieves significant improvements over a baseline static scheduler, reaching average improvements up to 18.4% in execution time and 30.1% in Energy-Delay Product (EDP) on a simulated 32-core system. The cost of reconfiguring hardware by means of a software-only solution rises with the number of cores due to lock contention and reconfiguration overhead. Therefore, novel architectural support is proposed to eliminate these overheads on future manycore systems. This architectural support minimally extends hardware structures already present in current processors, which allows further improvements in performance with negligible overhead. As a consequence, average improvements of up to 20.4% in execution time and 34.0% in EDP are obtained, outperforming state-of-the-art acceleration proposals not aware of task criticality.This work has been supported by the Spanish Government (grant SEV2015-0493, SEV-2011-00067 of the Severo Ochoa Program), by the Spanish Ministry of Science and Innovation (contracts TIN2015-65316, TIN2012-34557, TIN2013-46957-C2-2-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. Moret´o 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). E. Castillo has been partially supported by the Spanish Ministry of Education, Culture and Sports under grant FPU2012/2254.Peer ReviewedPostprint (author's final draft

Performance and energy task migration model for heterogeneous clusters

This article presents a set of linear regression models to predict the impact of task migration on different objectives, like performance and energy consumption. It allows to establish whether at a given moment the migration of a task is profitable in terms of performance or energy consumption. Also, it can be used to determine the best node to migrate a task depending on the objective. The model uses a small set of parameters that are easily measurable. It has been validated against a small heterogeneous cluster using the Slurm resource manager. The model captures the tendencies observed in the results of the experiments, with average relative errors below 3.5% in execution time and 2.5% in energy consumption.Acknowledgements This work has been supported by the Spanish Science and Tech[1]nology Commission under contract PID2019-105660RB-C22 and the European HiPEAC Network of Excellence

EngineCL: Usability and Performance in Heterogeneous Computing

Heterogeneous systems have become one of the most common architectures today, thanks to their excellent performance and energy consumption. However, due to their heterogeneity they are very complex to program and even more to achieve performance portability on different devices. This paper presents EngineCL, a new OpenCL-based runtime system that outstandingly simplifies the co-execution of a single massive data-parallel kernel on all the devices of a heterogeneous system. It performs a set of low level tasks regarding the management of devices, their disjoint memory spaces and scheduling the workload between the system devices while providing a layered API. EngineCL has been validated in two compute nodes (HPC and commodity system), that combine six devices with different architectures. Experimental results show that it has excellent usability compared with OpenCL; a maximum 2.8% of overhead compared to the native version under loads of less than a second of execution and a tendency towards zero for longer execution times; and it can reach an average efficiency of 0.89 when balancing the load.This work has been supported by the Spanish Ministry of Education (FPU16/ 03299 grant), the Spanish Science and Technology Commission (TIN2016-76635-C2-2-R) and the European HiPEAC Network of Excellence