Below 850 P2 I would not even think on high end PC with GTX1080Ti. But for me I would bought 1000 T2 or minimum EVGA 850 P2. Stay tuned to see how Nvidia’s latest Turing architecture stacks up.My choice for GTX1080Ti. With a raw performance of nearly 1.1 Million PPD in windows and an efficiency of almost 3500 PPD/Watt, this card is a good choice for doing science effectively. The Geforce GTX 1080 Ti is the fastest and most efficient graphics card that I’ve tested so far for Stanford’s distributed computing project. The best hardware is gear that maximizes disease research (performance) done per watt of power consumed. Here, efficiency is defined as Performance Out / Power In. Being a blog about doing the most work possible for the least amount of power, I am all about finding hardware that is highly efficient. Power consumption alone isn’t the whole story. Thus, it isn’t surprising to see that power consumption is at the top of the pack. With a board power rating of 250 Watts, this is a power hungry graphics card. As you can see, with nearly 1.1 Million PPD, this card does a lot of science. The following plot shows just how fast the 1080 Ti is compared to other graphics cards I have tested. For example, here are five screen shots of the client, showing five different instantaneous PPD values for the 1080 Ti. Note that any given work unit can produce more or less PPD than the average, with variation of 10% being very common. Here, I have averaged my Points Per Day results out over many work units to provide a consistent number. A typical performance metric is “Points per Day” (PPD). rewards donors with “Points” for their contributions, based on how much science is done and how quickly it is returned. I did extensive testing of the 1080 Ti over many weeks. Fans: 1x CPU, 2 x 120 mm intake, 1 x 120 mm exhaust, 1 x 80 mm exhaust.Drives: 1x SSD, 2 x 7200 RPM HDDs, Blu-Ray Burner.Power consumption measurements are taken on the system-level using a P3 Watt Meter at the wall. The Nvidia graphics driver version was 441.87. Testing is performed in my old but trusty benchmark machine, running Windows 10 Pro and using Stanford’s V7 Client. Only the RTX 2080 Ti is decidedly faster. In benchmarks, it holds its own against the much newer RTX cards, besting even the RTX 2080 and matching the RTX 2080 Super. With 3584 CUDA Cores, the 1080 Ti is an absolute beast. The 1080 Ti is at the top of Nvidia’s lineup of their 10-series cards. GPUs provide the majority of the computational performance of Geforce GTX 1080 Ti Specs However, GPUs, with their massively parallel shader cores, can do certain types of single-precision math much faster than CPUs. CPUs provide a good baseline of performance, and certain molecular simulations can only be done here. For computer nerds, this is an awesome way to give (money–>electricity–>computer work–>fighting disease).įolding at home (or can be run on both CPUs and GPUs. User’s computers solve molecular dynamics problems in the background, which help the Consortium understand how proteins “misfold” to cause disease. These are beastly cards, capable of running most games with max settings in 4K resolutions.īut, how does it fold? at home is a distributed computing project originally developed by Stanford University, where everyday users can lend their PC’s computational horsepower to help disease researchers understand and fight things like cancer, Alzheimer’s, and most recently the COVID-19 Coronavirus. Three years later, with the release of the RTX 2080 Ti, the 1080 Ti still holds its own, and still commands well over $400 on the used market.
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