Compare GPU Software: Jacket versus PCT™ (R2012a)
Hold your horses! The Parallel Computing Toolbox™ (PCT) already supports GPUs, so why should I use Jacket?
The answer is simple: Jacket is better
Specifically, Jacket beats the latest R2012a PCT product and is:
- Faster: Did you know that Jacket on a laptop beats PCT on a Tesla (link)? See more benchmarks below!
- More Stable: Jacket is rigorously tested and very mature. GPU functions in PCT are prone to many bugs and computational errors. Just read what PCT customers are complaining about on the MathWorks® forums!
- More Capable: Jacket contains 10X more functions than PCT R2012a. It also contains the innovative GFOR loop, GCOMPILE, and native multi-GPU capabilities through GSELECT.
- Built on Better Technology: Whereas R2012a PCT GPU functionality is fundamentally flawed in its use of a legacy Java-based system, Jacket contains an innovative and super light-weight runtime system. AccelerEyes is solely focused on delivering GPU speed, while MathWorks is distracted with other priorities.
Let's explore these areas in more depth.
Jacket is More Capable
At a high level, there are a number of key capabilities for GPU Computing with MATLAB® that are needed in most application implementations that are available in Jacket and not available in other systems. These capabilities are illustrated in the following chart with how each technology compares:
| Jacket | PCT | |
|---|---|---|
| Linear Algebra Support |  |
minimal |
| Image Processing Functions | |
minimal |
| Signal Processing Functions | |
minimal |
| Statistics Functions | |
minimal |
| Graphics Functions | |
 |
| Supports all NVIDIA CUDA GPUs | |
|
| Supports all AMD/ATI GPUs and other OpenCL devices | |
|
| Supports R2006b or later versions of MATLAB | |
|
| Works without PCT | |
|
| Data-parallel FOR loop (see GFOR) | |
|
Jacket is Faster
The following performance table illustrates results of benchmarks comparing Jacket to PCT using matrices of 1024-by-1024 unless otherwise noted. Timing is provided in seconds and performance improvement of Jacket is expressed as a speedup multiplier over the PCT equivalent version.
The configuration of the system used was a CPU: (2x) Intel Xeon X5680 CPU @ 3.33GHz and a GPU: NVIDIA TESLA C2070, as follows:
| Jacket (s) | PCT (s) | Speedup Multiplier | |
|---|---|---|---|
| ARRAYFUN (Horner Example), Single: | 0.18641 | 6.53213 | 35.0 X |
| ARRAYFUN (Horner Example), Double: | 0.42653 | 8.99316 | 21.1 X |
| FFT (Example), Single: | 10.25867 | 19.89959 | 1.9 X |
| FFT (Example), Double: | 11.96000 | 23.99205 | 2.0 X |
| MLDIVIDE (1023x1023 square matrix), Double: | 62.68685 | 74.66244 | 1.2 X |
| TRIG, Single: | 0.696341 | 2.112127 | 3.0 X |
| TRIG, Double: | 0.994353 | 2.767755 | 2.8 X |
| CTRANSPOSE, Single: | 1.741304 | 3.298389 | 1.9 X |
| CTRANSPOSE, Double: | 1.729959 | 3.896148 | 2.3 X |
| POWER, Single: | 1.525003 | 2.989234 | 2.0 X |
| POWER, Double: | 2.384597 | 4.051902 | 1.7 X |
| RESHAPE, Single: | 0.145981 | 1.948034 | 13.3 X |
| RESHAPE, Double: | 0.144335 | 2.545458 | 17.6 X |
| TIMES, Single: | 0.627013 | 1.990203 | 3.2 X |
| TIMES, Double: | 0.814789 | 2.54477 | 3.1 X |
| * benchmarks numbers provided by AccelerEyes' third-party partner. | |||
Other benchmarks are also available on the Jacket community forums, here and here.
Built on Better Technology
Learn more about how Jacket technology compares to PCT in these blog posts.
- Customer frustration with R2011A blog post.
- A case study comparison using code from MATLAB Central is available as a blog post and video.
- Stanford GPU benchmarks blog post.
- Super Mario Brothers analogy to Jacket's GPU runtime blog post.
Finally, as everyone already knows, MATLAB® and PCT™ are trademarks of MathWorks.
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