Interestingly enough it primarily uses double precision math – whether this is a good thing or not though is up to the reader given the GTX 680’s anemic double precision performance.īecause it’s based around double precision math the GTX 680 does rather poorly here, but the surprising bit is that it did so to a larger degree than we’d expect. We ultimately started looking at Distributed Computing applications and settled on PrimeGrid, whose CUDA accelerated GENEFER client worked with GTX 680.
#Nvidia gtx 680 compare professional
Since our goal here is to stick to consumer/prosumer applications in reflection of the fact that the GTX 680 is a consumer card, we did somewhat limit ourselves by ruling out a number of professional CUDA applications, but there’s no telling that compatibility there would fare any better.
Among the CUDA programs that failed were NVIDIA’s own Design Garage (a GTX 480 showcase package), AccelerEyes’ GBENCH MatLab benchmark, and the latest client. But 3 rd party applications are a much bigger issue. To be clear, NVIDIA’s “core” CUDA functionality remains intact PhysX, video transcoding, etc all work.
Just as many OpenCL programs were hand optimized and didn’t know what to do with the Southern Islands architecture, many CUDA applications didn’t know what to do with GK104 and its Compute Capability 3.0 feature set. Unfortunately we were largely met with failure, for similar reasons as we were when the Radeon HD 7970 launched. If nothing else it maintains NVIDIA’s general lead in this benchmark, and is the first sign that GTX 680’s compute performance isn’t all bad.įor our fourth compute benchmark we wanted to reach out and grab something for CUDA, given the popularity of NVIDIA’s proprietary API. GTX 680 is still technically slower than GTX 580, but only marginally so. Starting with our AES encryption benchmark NVIDIA begins a recovery. The results of this benchmark are the average time to encrypt the image over a number of iterations of the AES cypher. While that doesn’t make it clear how much of GTX 680’s performance is due to the compiler versus a general loss in compute performance, it does offer at least a slim hope that NVIDIA can improve their compute performance.įor our next benchmark we’re looking at AESEncryptDecrypt, an OpenCL AES encryption routine that AES encrypts/decrypts an 8K x 8K pixel square image file. Apparently NVIDIA has put absolutely no time into optimizing their now all-important Kepler compiler for SmallLuxGPU, choosing to focus on games instead. On that note, since we weren’t going to significantly change our benchmark suite for the GTX 680 launch, NVIDIA had a solid hunch that we were going to use SmallLuxGPU in our tests, and spoke specifically of it. In fact the GTX 680 has more in common with the GTX 560 Ti than it does anything else. At this point the GTX 680 can’t even compete with the GTX 570, let alone anything Radeon. We’re now using a development build from the version 2.0 branch, and we’ve moved on to a more complex scene that hopefully will provide a greater challenge to our GPUs.ĬivV was bad SmallLuxGPU is worse. Our next benchmark is SmallLuxGPU, the GPU ray tracing branch of the open source LuxRender renderer. It’s not by much, mind you, but in this case the GTX 680 for all of its functional units and its core clock advantage doesn’t have the compute performance to stand toe-to-toe with the GTX 580.Īt first glance our initial assumptions would appear to be right: Kepler’s scheduler changes have weakened its compute performance relative to Fermi. AMD’s shift to GCN has rocketed them to the top of our Civ V Compute benchmark, meanwhile the reality is that in what’s probably the most realistic DirectCompute benchmark we have has the GTX 680 losing to the GTX 580, never mind the 7970. Remember when NVIDIA used to sweep AMD in Civ V Compute? Times have certainly changed. Note that this is a DX11 DirectCompute benchmark. Civ V includes a sub-benchmark that exclusively tests the speed of their texture decompression algorithm by repeatedly decompressing the textures required for one of the game’s leader scenes. Our first compute benchmark comes from Civilization V, which uses DirectCompute to decompress textures on the fly. NVIDIA has made it clear that they are focusing first and foremost on gaming performance with GTX 680, and in the process are deemphasizing compute performance. As we mentioned in our discussion on the Kepler architecture, GK104’s improvements seem to be compute neutral at best, and harmful to compute performance at worst. As always our final set of benchmarks is a look at compute performance.
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