Compute

Shifting gears, we have our look at compute performance. Since GTX Titan X has no compute feature advantage - no fast double precision support like what's found in the Kepler generation Titans - the performance difference between the GTX Titan X and GTX 980 Ti should be very straightforward.

Starting us off for our look at compute is LuxMark3.0, the latest version of the official benchmark of LuxRender 2.0. LuxRender’s GPU-accelerated rendering mode is an OpenCL based ray tracer that forms a part of the larger LuxRender suite. Ray tracing has become a stronghold for GPUs in recent years as ray tracing maps well to GPU pipelines, allowing artists to render scenes much more quickly than with CPUs alone.

Compute: LuxMark 3.0 - Hotel

With the pace set for GM200 by GTX Titan X, there’s little to say here that hasn’t already been said. Maxwell does not fare well in LuxMark, and while GTX 980 Ti continues to stick very close to GTX Titan X, it none the less ends up right behind the Radeon HD 7970 in this benchmark.

For our second set of compute benchmarks we have CompuBench 1.5, the successor to CLBenchmark. CompuBench offers a wide array of different practical compute workloads, and we’ve decided to focus on face detection, optical flow modeling, and particle simulations.

Compute: CompuBench 1.5 - Face Detection

Compute: CompuBench 1.5 - Optical Flow

Compute: CompuBench 1.5 - Particle Simulation 64K

Although GTX T980 Ti struggled at LuxMark, the same cannot be said for CompuBench. Though taking the second spot in all 3 sub-tests - right behind GTX Titan X - there's a bit wider of a gap than normal between the two GM200 cards, causing GTX 980 Ti to trail a little more significantly than in other tests. Given the short nature of these tests, GTX 980 Ti doesn't get to enjoy its usual clockspeed advantage, making this one of the only benchmarks where the theoretical 9% performance difference between the cards becomes a reality.

Our 3rd compute benchmark is Sony Vegas Pro 13, an OpenGL and OpenCL video editing and authoring package. Vegas can use GPUs in a few different ways, the primary uses being to accelerate the video effects and compositing process itself, and in the video encoding step. With video encoding being increasingly offloaded to dedicated DSPs these days we’re focusing on the editing and compositing process, rendering to a low CPU overhead format (XDCAM EX). This specific test comes from Sony, and measures how long it takes to render a video.

Compute: Sony Vegas Pro 13 Video Render

Traditionally a benchmark that favors AMD, GTX 980 Ti fares as well as GTX Titan X, closing the gap some. But it's still not enough to surpass Radeon HD 7970, let alone Radeon R9 290X.

Moving on, our 4th compute benchmark is FAHBench, the official Folding @ Home benchmark. Folding @ Home is the popular Stanford-backed research and distributed computing initiative that has work distributed to millions of volunteer computers over the internet, each of which is responsible for a tiny slice of a protein folding simulation. FAHBench can test both single precision and double precision floating point performance, with single precision being the most useful metric for most consumer cards due to their low double precision performance. Each precision has two modes, explicit and implicit, the difference being whether water atoms are included in the simulation, which adds quite a bit of work and overhead. This is another OpenCL test, utilizing the OpenCL path for FAHCore 17.

Compute: Folding @ Home: Explicit, Single Precision

Compute: Folding @ Home: Implicit, Single Precision

Folding @ Home’s single precision tests reiterate GM200's FP32 compute credentials. Second only to GTX Titan X, GTX 980 Ti fares very well here.

Compute: Folding @ Home: Explicit, Double Precision

Meanwhile Folding @ Home’s double precision test reiterates GM200's poor FP64 compute performance. At 6.3ns/day, it, like the GTX Titan X, occupies the lower portion of our benchmark charts, below AMD's cards and NVIDIA's high-performnace FP64 cards.

Wrapping things up, our final compute benchmark is an in-house project developed by our very own Dr. Ian Cutress. SystemCompute is our first C++ AMP benchmark, utilizing Microsoft’s simple C++ extensions to allow the easy use of GPU computing in C++ programs. SystemCompute in turn is a collection of benchmarks for several different fundamental compute algorithms, with the final score represented in points. DirectCompute is the compute backend for C++ AMP on Windows, so this forms our other DirectCompute test.

Compute: SystemCompute v0.5.7.2 C++ AMP Benchmark

We end up ending our benchmarks where we started: with the GTX 980 Ti slightly trailing the GTX Titan X, and with the two GM200 cards taking the top two spots overall. So as with GTX Titan X, GTX 980 Ti is a force to be reckoned with for FP32 compute, which for a pure consumer card should be a good match for consumer compute workloads.

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  • FlushedBubblyJock - Wednesday, June 10, 2015 - link

    I bought a bunch of G80 G92 G92b and G94 nvidia cards because you could purchase memory size, bandwidth, bit width, power connector config, essentially any speed at any price point for a gamers rig, install the same driver, change the cards easily, upgrade for your customers without hassles...

    IT WAS A GOLD MINE OF FLEXIBILITY

    What happened was, the amd fanboys got very angry over the IMMENSE SUCCESS of the initial G80 and it's reworked cores and totally fluid memory, card size, bit width, and pricing configurations... so they HAD TO TRY TO BRING IT DOWN...

    Thus AMD launched their PR war, and the clueless amd fan launched their endless lies.

    I'll tell you this much, no on would trade me a 9800GTX for a 9800GT

    I couldn't get the 92 bit width cards for the same price as the 128 bit

    DDR2 and DDR3 also differentiated the stack massively.

    What we had wasn't rebranding, but an amazingly flexible GPU core that stood roaring above at the top and could be CUT down to the middle and the low gaming end, an configured successfully with loads of different bit widths and memory configs....

    64 bit width, 92, 128, 256, 384, 192, ETC...

    That was an is a awesome core, period.
  • BillyONeal - Sunday, May 31, 2015 - link

    And people have been bent out of shape about it. For "YEARS" :)
  • dragonsqrrl - Sunday, May 31, 2015 - link

    Their highest-end rebadge, the 390X, will likely compete with the 980, not the 980 Ti. The 980 Ti will be closer to Fiji's performance profile.
  • austinsguitar - Sunday, May 31, 2015 - link

    I dont think you realize how much more efficiant this card is even compared to past cards for its nm and performance. This is a feat. Just calm down and enjoy. I am very happy that the cards price us perfect. :) thanks nvidia
  • MapRef41N93W - Sunday, May 31, 2015 - link

    Maybe you aren't aware of how silicon works, but this a 601mm^2 die which costs a boat load to produce especially with the rising costs of crystalline silicon dies. Being on 28nm this long just means the yields are higher (which is why a 601mm^2 is even possible).

    You aren't going to see a 14nm card that outperforms this by much till 2017 at the earliest which following the recent NVIDIA trends should see the Titan XYZ (whatever they want to call it) which should be a pretty huge jump at a pretty high price.
  • Thomas_K - Monday, June 1, 2015 - link

    Actually AMD is doing 14nm starting next year

    http://www.guru3d.com/news-story/it-is-official-am...
    "Although this was a rumor for a long time now we now know that AMD skips 20nm and jumps onto a 14nm fabrication node for their 2016 GPUs."
  • dragonsqrrl - Sunday, May 31, 2015 - link

    Not sure I understand your comment, 28nm is precisely why we're paying this much for this level of performance in 2015... But it's also pretty impressive for the same reason.
  • Azix - Sunday, May 31, 2015 - link

    14/16nm might cost more. 28nm should have better yields and lower cost. These chips do not cost much to make at all (retail price could be 2-3 times the chip cost)
  • dragonsqrrl - Sunday, May 31, 2015 - link

    I think you misinterpreted my comment. I was responding to someone who seemed shocked by the fact that price/performance ratios aren't improving dramatically despite the fact that we're on a very mature process. In response I said the fact that we're on the same process is precisely why we aren't seeing dramatic improvements in price/performance ratios.

    "28nm should have better yields and lower cost. These chips do not cost much to make at all (retail price could be 2-3 times the chip cost)"
    Yields are just one part of the equation. Die size also plays a significant role in manufacturing costs. The fact that your trying to say with a straight face that GM200 does not cost much to make says more than your written comment itself.
  • zepi - Monday, June 1, 2015 - link

    Assuming perfect scaling 600mm2 28nm chip would shrink to 150mm2 at 14nm.

    GM107 is a 148mm2 chip, so basically this "monster" with just a dieshrink would find a nice place for itself at the bottom end of Nvidias lineup with after transition to 14nm.

    This does not take into account the fact that at 14nm and 150mm2 they couldn't give it enough memory bandwidth so easily, but just tells you something about how significant the reduction in size and manifacturing cost is after the initial ramp-up of the yields.

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