CrowdCL: Web-Based Volunteer Computing with WebCL

Tommy MacWilliam Cris Cecka School of Engineering and Applied Sciences School of Engineering and Applied Sciences Harvard University Harvard University Cambridge, Massachusetts 02138 Cambridge, Massachusetts 02138 [email protected] [email protected]

Abstract—We present CrowdCL, an open-source framework greatly simplifies the process of communicating with the GPU, for the rapid development of volunteer computing and OpenCL defining OpenCL programs, and launching device computa- applications on the web. Drawing inspiration from existing GPU tions. Using both CrowdCL and KernelContext, developers can libraries like PyCUDA, CrowdCL provides an abstraction layer rapidly write parallel and cross-platform programs that can be for WebCL aimed at reducing boilerplate and improving code distributed to and executed by a collection of volunteers. readability. CrowdCL also provides developers with a framework to easily run computations in the background of a web page, Ultimately, the goals of CrowdCL are similar those of which allows developers to distribute computations across a existing volunteer computing projects. One such effort is Fold- network of clients and aggregate results on a centralized server. ing@home [7], which runs protein folding computations as We compare the performance of CrowdCL against serial imple- mentations in Javascript and Java across a variety of platforms. background processes on participants’ computers. The project Our benchmark results show strong promise for the web browser currently sustains more than 5 PetaFLOPS as a result of as a high-performance distributed computing platform. the contributions of an estimated 400,000 clients. Similarly, the BOINC project that powers SETI@home [5] has reached I.INTRODUCTION hundreds of TeraFLOPS through the participation of 500,000 users. Like BOINC, CrowdCL seeks to provide a generalized Through volunteer computing, users can contribute oth- framework that can be used to solve a variety of computation erwise idle CPU cycles to solving computationally-intensive problems. While Folding@home and SETI@home run as problems. By distributing jobs across a large network of users, desktop applications, efforts such as Mechanical Turk [11] volunteer computing projects can utilize more computational have used the web as a platform for crowdsourcing. While power than would be feasible to gather locally due to cost Mechanical Turk is aimed primarily at consumers, as users and space constraints. Typically, however, participation in a can create or solve micro-tasks in exchange for payment, volunteer computing project requires users to download and CrowdCL instead targets developers who seek to distribute install an additional package on their machine to run in the GPU computations via embedded web applications. background. However, the overhead associated with the instal- lation process may dissuade some users from participating, and In this paper, we will show that the CrowdCL frame- users may forget to launch or inadvertently quit the background work allows simplified development of distributed, OpenCL process, which also lowers participation rates. In addition, the accelerated applications. These applications computationally cost of supporting a seemingly infinite set of achitectures and outperform serial implementations in Javascript and Java on a configurations can be significant [7]. single machine, and can be embedded on any web page using a single