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Bear in mind that this is more quick-and-dirty benchmarking, not rigorously repeated to validate results. The results, however, look interesting and the issue of compute on new GPUs bears further investigating. I used four different GPUs: As you can see from the table below, all four GPUs ran at the reference frequencies, including memory.
When I show the results, I don't speculate on the impact of compute versus memory bandwidth or quantity. The first benchmark, CompuBench CL from Hungary-based Kishonti, actually consists of a series of benchmarks, each focusing on a different compute problem.
Because the compute tasks differ substantially, CompuBench doesn't try to aggregate them into a single score. So I show separate charts for each test. According to Kishonti, "Face detector is based on the Viola-Jones algorithm. Face detection is extensivesly used in biometrics and digital image processing to determine locations and sizes of human faces". The second vision processing test, TV-L1 optical flow, is "based on dense motion vector calculation using variational method.
Optical flow is widely used for video compression and enhancing video quality in vision-based use cases, such as driver assistance systems or motion detection". So far, it's looking pretty linear, with the GTX leading the other cards by pretty wide margins. Can the latest consumer GPU from Nvidia stay the course? CompuBench includes two physics-oriented OpenCL benchmarks. Let's first look at Ocean Simulation. Kishonti notes, "Test of the FFT algorithm based on ocean wave simlation.
The Fast Fourier transform computes transformations of time or space to frequency and vice-versa. FFTs are widely used in engineering, science, and mathematics". Well, it looks like a few cracks are showing up in Nvidia's compute performance capabilities. Let's look at particle simulation. The benchmark notes read, "Particle Simulation in a spatial grid using the discrete element method. The result of the simulation is visualized as shaded point sprite spheres with OpenGL".
Okay, the FFT-based ocean simulation test could just be an outlier. This particular test, in Kishonti's words: On the other hand, I've never been one to test at identical clock frequencies. It's all well and good to talk about architectural efficiency, but when one processor can run MHz faster, marginally lower ISA efficiency doesn't really mean much. Kishonti describes this benchmark as "… replicating a typical video composition pipeline with effects such as pixelat, mask, mix, and blur".
Once again, it appears that the Radeon Fury Nano offers better execution efficiency, but the raw clock speed of the GTX makes up the difference. LuxMark uses the LuxRender physically-based rendering tool to run its benchmark. I might revisit the medium and high-end scenes later.
I'm not quite sure what's going on with LuxMark, and it's clearly worth going back and checking other scenes. I did run these tests twice to double-check.
The emerging pattern we've seen suggests AMD's GCN architecture offers better efficiency, but the GTX is running on early release drivers focused on gaming performance. Even so, any new drivers need to cover a lot of ground to catch up with the Radeon Fury Nano. There's no question GPUs have proven useful as general purpose compute engines, which means there's money to be made in selling dedicated GPU compute hardware.
That's pretty serious money. The folks at ArrayFire wrote a pretty illuminating post about the differences between floating point performance differences on Kepler-based GPUs. Nvidia's segmentation has only gotten stricter since then. To be fair, though, removing compute capabilities unneeded for games allows Nvidia to build a superb gaming GPU that's just mm2. Incorporating additional features would also increase the die size, adding cost.
However, it also means users can't just go out and buy a bunch of consumer GPUs and expect near-parity compute performance with Nvidia's Tesla-class products. Also, bear in mind we're looking at essentially two OpenCL 1. It's possible the landscape for compute could change. Nvidia also has a lot of capital invested in its proprietary CUDA software architecture, though what impact that has on Nvidia's OpenCL development is unknown.
Ignoring die size for a moment, Nvidia designed the GTX using 7. That suggests the Fury may have additional capabilities that the GTX lacks, which may be another reason for the OpenCL performance disparities shown. This could partly be because of resources: Re-examining GPU compute performance when AMD ships its Polaris and Vega products, which should run at much higher clock frequencies, will be interesting, indeed.
This post originally appeared on Uncertainty on May 23rd, and is republished here with permission. Snub-Nosed Blade Runner Blasters! Custom Stay Puft Marshmallow Man! Adam Savage's One Day Builds: Making a Skyrim-Inspired Foam Sword!
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