Researchers in the Pervasive Parallelism Laboratory at Stanford University recently published work describing a novel framework for parallel computing with a paper entitled, “A Domain-Specific Approach to Heterogeneous Parallelism.” As part of their research, they compared Jacket to the GPU support in the Parallel Computing Toolbox™. The results clearly show that Jacket’s optimizations make a big difference in performance.
In this blog post, we highlight 4 algorithms included in their research:
| NAME | DESCRIPTION | INPUT |
| Gaussian Discriminant Analysis (GDA) | Generative learning algorithm for modeling the probability distribution of a set of data as a multivariate Gaussian | 1,200×1,024 Matrix |
| Restricted Boltzmann Machine (RBM) | Stochastic recurrent neural network, without connections between hidden units | 2,000 Hidden Units
2,000 Dimensions |
| Support Vector Machine (SVM) | Optimal margin classifier, implemented using the Sequential Minimal Optimization (SMO) algorithm | 800×1,448 Matrix |
| Naïve Bayes (NB) | Fast, low-work supervised learning algorithm for classification | 25,000×1,448 Matrix |
These algorithms were benchmarked using the following system at Stanford:
| System Specs: | ||
| Computer | Dell Precision T7500n | |
| Processor | 2 Quad-core Intel Xeon X5550 2.67 GHz | Each core has 2-way hyperthreading for a total of 16 hardware thread contexts |
| RAM | 24GB | |
| GPU | NVIDIA GTX 275 |
The execution times for these algorithms are shown in the charts below:
To learn more about how Jacket compares, visit: http://www.accelereyes.com/products/compare. Special thanks to the Stanford researchers for undertaking this effort, and good luck continuing this line of work! We look forward to learning from your insights.
Researchers: Hassan Chafi, Arvind K. Sujeeth, Kevin J. Brown, HyoukJoong Lee, Anand R. Atreya, and Kunle Olukotun