9027

Just-in-time Acceleration of JavaScript

Uday Pitambare, Arun Chauhan, Saurabh Malviya
School of Informatics and Computing, Indiana University, Bloomington, IN 47405
Indiana University, Technical Report TR706, 2013
@article{pitambare2013just,

   title={Just-in-time Acceleration of JavaScript},

   author={Pitambare, Uday and Chauhan, Arun and Malviya, Saurabh},

   year={2013}

}

Download Download (PDF)   View View   Source Source   

541

views

JavaScript has seen tremendous growth in popularity driven by increasingly interactive web sites and sophisticated web interfaces. However, the performance of JavaScript continues to be a hurdle in using it for tasks that are computationally intensive, such as gaming, simulations, and visualization. JavaScript has also been slow to exploit the available parallelism on modern computers. Specifically, it is not currently easy to exploit GPGPUs within JavaScript. A part of the reason is that the low-level interface demanded for GPGPU programming is often not approachable by JavaScript programmers. In this paper, we present a novel approach that provides a mechanism to accelerate portions of JavaScript programs without requiring the programmers to learn new syntax or low-level APIs. We achieve that through an embedded DSL used to specify GPGPU computations. We have designed a JavaScript library, and an accompanying Firefox extension, that work together to compile the embedded DSL just-in-time using the LLVM backend for generating PTX. The compiled code is cached to minimize the compilation overhead. Our evaluation of the system using a micro-benchmark, two applications kernels, and an application benchmark demonstrates that our approach imposes minimal performance overhead, while providing an easy GPGPU programming interface to JavaScript programmers.
VN:F [1.9.22_1171]
Rating: 4.7/5 (3 votes cast)
Just-in-time Acceleration of JavaScript, 4.7 out of 5 based on 3 ratings

* * *

* * *

Like us on Facebook

HGPU group

195 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1337 peoples are following HGPU @twitter

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: AMD APP SDK 2.9
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.2
  • SDK: nVidia CUDA Toolkit 6.0.1, AMD APP SDK 2.9

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2014 hgpu.org

All rights belong to the respective authors

Contact us: