4 research outputs found
The Seamless Peer and Cloud Evolution Framework
Evolutionary algorithms are increasingly being applied to problems that are too computationally expensive to run on a single personal computer due to costly fitness function evaluations and/or large numbers of fitness evaluations. Here, we introduce the Seamless Peer And Cloud Evolution (SPACE) framework, which leverages bleeding edge web technologies to allow the computational resources necessary for running large scale evolutionary experiments to be made available to amateur and professional researchers alike, in a scalable and cost-effective manner, directly from their web browsers. The SPACE framework accomplishes this by distributing fitness evaluations across a heterogeneous pool of cloud compute nodes and peer computers. As a proof of concept, this framework has been attached to the RoboGen open-source platform for the co-evolution of robot bodies and brains, but importantly the framework has been built in a modular fashion such that it can be easily coupled with other evolutionary computation systems
Evaluating and Enabling Scalable High Performance Computing Workloads on Commercial Clouds
Performance, usability, and accessibility are critical components of high performance computing (HPC). Usability and performance are especially important to academic researchers as they generally have little time to learn a new technology and demand a certain type of performance in order to ensure the quality and quantity of their research results. We have observed that while not all workloads run well in the cloud, some workloads perform well. We have also observed that although commercial cloud adoption by industry has been growing at a rapid pace, its use by academic researchers has not grown as quickly. We aim to help close this gap and enable researchers to utilize the commercial cloud more efficiently and effectively.
We present our results on architecting and benchmarking an HPC environment on Amazon Web Services (AWS) where we observe that there are particular types of applications that are and are not suited for the commercial cloud. Then, we present our results on architecting and building a provisioning and workflow management tool (PAW), where we developed an application that enables a user to launch an HPC environment in the cloud, execute a customizable workflow, and after the workflow has completed delete the HPC environment automatically. We then present our results on the scalability of PAW and the commercial cloud for compute intensive workloads by deploying a 1.1 million vCPU cluster. We then discuss our research into the feasibility of utilizing commercial cloud infrastructure to help tackle the large spikes and data-intensive characteristics of Transportation Cyberphysical Systems (TCPS) workloads. Then, we present our research in utilizing the commercial cloud for urgent HPC applications by deploying a 1.5 million vCPU cluster to process 211TB of traffic video data to be utilized by first responders during an evacuation situation. Lastly, we present the contributions and conclusions drawn from this work
Enabling Analytic and HPC Workflows with COMPSs
In the recent joint venture between High-Performance Computing (HPC) and Big-Data
(BD) Ecosystems towards the Exascale Computing, the scientific community has realized
that powerful programming models and high-level abstraction tools are a must. Within this
context, the Barcelona Supercomputing Center (BSC) is developing the COMP Superscalar
(COMPSs) programming model, whose main objective is to develop applications in a sequential
way, while the Runtime System handles the inherent parallelism of the application
and abstracts the programmer from the different underlying infrastructures. The parallelism
is achieved by defining an application Interface that allows COMPSs to detect methods that
operate on a set of parameters (called tasks), and execute them distributedly and transparently.
This Master Thesis aims to enhance COMPSs, adapting it to the needs of the Big-Data
Ecosystems, by supporting Analytic and HPC workflows. To this end, we propose a straightforward
integration with the execution of binaries, and MPI and OmpSs applications. Although
the COMPSs programming model is kept untouched, we extend the COMPSs Annotations
and some of the COMPSs internals such as the task schedulers and the worker
executors.
To support our contribution, we have ported to COMPSs two real use cases. On the
one hand, NMMB BSC-Dust, a workflow to predict the atmospheric life cycle of the desert
dust and, on the other hand, Guidance, an integrated solution for Genome and Phenome
association analysis