The emerging pattern we’ve seen suggests AMD’s GCN architecture offers better efficiency, but the GTX 1080 is running on early release drivers focused on gaming performance. I did run these tests twice to double-check. I’m not quite sure what’s going on with LuxMark, and it’s clearly worth going back and checking other scenes. LuxMark 3.1 uses LuxRender 1.5, which seems to be based on OpenCL 1.1, though documentation on API usage is sketchy. I might revisit the medium and high-end scenes later. In the interest of time, I only ran the default LuxBall HDR test, a relatively low triangle-count scene incorporating 217K triangles. LuxMark uses the LuxRender physically-based rendering tool to run its benchmark. The GTX 1080 wins out, but AMD beats the older Nvidia GPUs. Bitcoin MiningĬompuBench CL’s bitcoin mining test offers a pretty straightforward integer hashing benchmark. But the Nano even beats out the 12GB monster that is the Titan X. Once again, it appears that the Radeon Fury Nano offers better execution efficiency, but the raw clock speed of the GTX 1080 makes up the difference. Kishonti describes this benchmark as “… replicating a typical video composition pipeline with effects such as pixelat, mask, mix, and blur”. It’s all well and good to talk about architectural efficiency, but when one processor can run 600MHz faster, marginally lower ISA efficiency doesn’t really mean much. On the other hand, I’ve never been one to test at identical clock frequencies. Once again, the Fury Nano surprises a bit, easily outperforming the GTX 980, and trailing the shiny new GTX 1080 by under 7%, while giving up 600MHz in clock frequency. This particular test, in Kishonti’s words: “features dynamically updated acceleration structure and global illumination”. Graphics: T-Rex Path TracingĬompuBench CL provides a single test for graphics, based on the T-Rex benchmark the company developed for mobile GPU testing. Clearly, though, there’s something about AMD’s GCN architecture that makes it an efficient FFT engine - at least, more efficient than Nvidia’s consumer GPUs. Okay, the FFT-based ocean simulation test could just be an outlier. The result of the simulation is visualized as shaded point sprite spheres with OpenGL”. The benchmark notes read, “Particle Simulation in a spatial grid using the discrete element method. Well, it looks like a few cracks are showing up in Nvidia’s compute performance capabilities. FFTs are widely used in engineering, science, and mathematics”. The Fast Fourier transform computes transformations of time or space to frequency and vice-versa. Kishonti notes, “Test of the FFT algorithm based on ocean wave simlation. CompuBench includes two physics-oriented OpenCL benchmarks. Nvidia spends a lot of PR capital touting physics processing with its GPUs. Can the latest consumer GPU from Nvidia stay the course? Physics: Ocean Surface Simulation and Particle Simulation – 64K So far, it’s looking pretty linear, with the GTX 1080 leading the other cards by pretty wide margins. 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”. The second vision processing test, TV-L1 optical flow, is “based on dense motion vector calculation using variational method. Face detection is extensivesly used in biometrics and digital image processing to determine locations and sizes of human faces”. Vision Processing: Face Detection and TV-L1 Optical FlowĪccording to Kishonti, “Face detector is based on the Viola-Jones algorithm. CompuBench CL 1.5 desktop uses OpenCL 1.1. Because the compute tasks differ substantially, CompuBench doesn’t try to aggregate them into a single score. The first benchmark, CompuBench CL from Hungary-based Kishonti, actually consists of a series of benchmarks, each focusing on a different compute problem. When I show the results, I don’t speculate on the impact of compute versus memory bandwidth or quantity. GPUĪs you can see from the table, all four GPUs ran at the reference frequencies, including memory. The GTX 1080 used the early release drivers, while the other GPUs ran on the latest WHQL-certified drivers available from the GPU manufacturer’s web site. I used four different GPUs: GTX 1080, Titan X, GTX 980, and an AMD Radeon Fury Nano. These tests ran on my existing production system, a Core i7-6700K with 32GB DDR4 running at the stock 2,133MHz effective. The results, however, look interesting and the issue of compute on new GPUs bears further investigating. Bear in mind that this is more quick-and-dirty benchmarking, not rigorously repeated to validate results. Out of idle curiosity, I ran a couple of OpenCL compute-oriented benchmarks on the GTX 1080 and three other GPUs.
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