Does anybody know the clock speed on anyde's Titans?
Yes, I know
I changed the settings with EVGA precision to:
shader +160 MHz
memory + 160 MHz
Power = 100% (default)
Temp = 80 C (default)
Fans = 85% (max)
While the fans are noisy at this settings the cards keep running at ca. 70 degree C (Usually, I keep the system loaded for 24 hrs operation, so temperature trumps noise to keep the system's components as cool as possible)
There is a point in Unigine Heaven's 4.0 performance curve where the GPU's don't matter anymore for increased scores. The "limiting" factor is the software architecture of the benchmark application as it uses only 2 threads on the Intel CPU. The only way to raise Unigine Valley results in this setting is to raise the frequency of a few CPU cores. Increasing the GPU frequency doesn't help, neither does help an increase in the number of Intel CPU cores as they aren't used at all. This is typical for many benchmarks as they aren't optimized for the throughput capabilities of systems. 3dMark performs better on a highly overclocked 4core CPU than on a 16core dual Xeon system. If you are only interested in benchmark results, take the smaller and cheaper CPU with OC capabilities.
The result shown in my previous post (4257 points) was achieved with above settings and the i7-3930K CPU set at 4,1 GHz. With identical settings, but the CPU set to its original 3.2 GHz frequency, the result goes down to the 3300 range. Looking at the power consumption of the GPUs during the run (relatively low) a higher CPU frequency would surely push the result higher. As said, it's a software architecture thing, not a GPU capability alone.
I am not using the system for graphics workload but compute stuff. Out of interest, I checked some of the more common GPU benchmarks like Unigine, 3dMark and Furmark.
To visualize my argument above.
Quite a lot of discussions filled threads in internet forums like here about the utility of a GTX Titan vs. alternatives like the GTX 680 or GTX 690. "Overpriced", "marginal impact", "cheaper alternatives available" were often read arguments.
With that, I thought I share a graphic I found by the Super Computer Center in San Diego, which shows the performance graphs of CPUs and GPUs for
Amber - one of the widely used applications in the field of bioinformatics. The slide is from a recent
presentation at GTC 2013 in March.
A few comments:
- For performance reasons, Amber leverages mixed precision calculation (i.e. single precision for individual multiplications of vector elements and double precision for its summation)
- A single GTX Titan seems to be 37% faster than a 8 node dual Xeon E2-2670 cluster (the 16 CPUs alone are 25600 US$ at newegg.com)
- One Titan is slightly faster than 4 GTX 680 in one compute node
- It is 22% faster than 2 x K10 cards (the "pro" version of the GTX 690, roughly equivalent to the GTX 690. A K10 card is currently listed at Amazon with 3000 US$)
- Due to its higher frequency, it is 28% faster than its professional K20X brethren. Due to scaling issues between 2 GPUs, it is still faster than 2 of them. (The K20X is the more expensive version (4600 US$) of the K20 which is currently listed at newegg.com for 3500 US$ each)
- The performance metric measured is nanoseconds of folding simulation per one day of compute time. The time steps are usually 2 femtoseconds, so for one nanosecond there need to be 500.000 iterations of the force calculations between the atoms under investigation.
rgds,
Andy