At the time we knew quite a bit about its functionality, but not its pricing, configuration, or performance.This brings us to today. Therefore i suspect set-up related issues…The SDK examples are not constructed as benchmarks suitable for comparing performance across GPUs.
K20 meanwhile will … Nvidia Tesla K20 The graphics card supports multi-display technology.
Overview Prices Specs + Add to comparison. K20X will be NVIDIA’s leading Tesla K20 product, offering the best performance at the highest power consumption (235W).
Home > Graphics card comparison > Nvidia Quadro P4000 vs Nvidia Tesla K20. K20X will be NVIDIA’s leading Tesla K20 product, offering the best performance at the highest power consumption (235W). We first saw Tesla K20 at NVIDIA’s 2012 GPU Technology Conference, where NVIDIA first announced the K20 along with the already shipping K10. Tried a few benchmarks of our code and NVIDIA sdks and K20 was always 10-40% slower depending on the work load…Is that possible or have we done something wrong setting it up…!! This will give K20 theoretical performance numbers around 3.52 TFLOPS for FP32, 1.17 TFLOPS for FP64, fed by 208GB/sec of memory bandwidth.First and foremost, K20 will not be a single GPU but rather it will be a family of GPUs. Check the clock frequencies, while executing a compute load, with nvidia-smi. So I wouldn’t expect massive performance differences between GTX680 and K20c in this regard. K20 meanwhile will be cheaper, a bit slower, and perhaps most importantly lower power at 225W. Here is the tail of the output from nvidia-smi -q while running a CUDA app on my K20c:How do i Check the clock frequencies, while executing a compute load, with nvidia-smi ?Just got our supermicro station with a new K20 gpu card.
Search. For those of you who have kept an eye on Titan, these are the same specs as the GPUs Titan, and though NVIDIA would not name it at the time we can now confirm that Titan is in fact composed of K20X GPUs and not K20.Moving on, at the moment NVIDIA is showing off the passively cooled K20 family design, confirming in the process that both K20 and K20X can be passively cooled as is the standard for servers. This allows you to configure multiple monitors in order to create a more immersive gaming experience, such as … I assume that you are performing a tightly controlled experiment, where you simply swap the GPUs in the same system, so all other system components (hardware and software) are exactly the same. Tried a few benchmarks of our code and NVIDIA sdks and K20 was always 10-40% slower depending on the work load…Is that possible or have we done something wrong setting it up…!! At the time NVIDIA was still bringing up the GPU behind K20 – GK110 – with the early announcement at GTC offering an early look at the functionality it would offer in order to prime the pump for developers. en. Open Box: NVIDIA GK110 TESLA K20 (900-22081-2220-000) 5GB 320-bit GDDR5 PCI Express 2.0 x16 Workstation Video Card Chipset Manufacturer: NVIDIA Core Clock: 706MHz NVIDIA’s initial wave of focus for the Telsa K20 is going to be on servers (it is SC12 after all), but with K20 also being an integral part of NVIDIA’s next-generation Maximus strategy we’re sure to see actively cooled workstation models soon enough.
Are both power connectors plugged in?
Find out which is better and their overall performance in the graphics card ranking. Categories. I just checked the smi -q output and it is similar to yours, regardless if a cuda app is running or not… isn’t that odd too…?Off the top of my head I do not have any ideas why a computationally bound code would be slower on a GK110 compared to GK104, provided there is enough parallelism to fill the larger GK110 well. On that note, despite the fact that the difference is all of 10W, 225W is a very important cutoff in the HPC space – many servers and chasses are designed around that being their maximum TDP for PCIe cards – so it was important for NVIDIA to offer as fast a card as possible at this TDP, alongside the more powerful but more power hungry K20X. The 10x difference oberserved may be an artifact of the way the SDK example works; as I said these examples apps are not designed as benchmarks.With ECC off (which is what you tried), the theoretical memory bandwidth of the K20c is about 8% higher than that of the GTX 680:You stated that the app slows down by between 1% and 40%.