The answer is in the double precision capabilities. The 780 Ti is physically locked at 1:24 FP32 where has the Titan Black has an ace up it’s sleeve.įor the Titan Black, the magic happens in the driver. The Titan black’s driver gives the user an option to choose the double precision performance between 1:3 and 1:24 FP32 (by switching the GPU to TCC mode). When the double precision performance is set to 1:24 FP32, which is the same as the 780 Ti, the the single precision performance of the Titan Black and 780 Ti are identical. But when the user sets the double precision performance to 1:3 FP32, the single precision performance is compromised to boost double precision performance and make it equal to the K40c. In other words, you can choose the performance of the Titan Black to match either the 780 Ti or the K40c based and your preference. The K40c has a double precision performance of 1:3 without compromising the single precision performance. ![]() This is because the K40 is given a special double precision unit for every 3 single precision cores ( white paper). NVIDIA also states that the Tesla GPUs go through a much more rigorous Q&A process which guarantees lesser failures and also has additional features such as ECC memory. NVIDIA’s GTX series are known for their great FP32 performance but are very poor in their FP64 performance. The performance generally ranges between 1:24 (Kepler) and 1:32 (Maxwell). The exceptions to this are the GTX Titan cards which blur the lines between the consumer GTX series and the professional Tesla/Quadro cards. ![]() The Kepler architecture Quadro and Tesla series card provide full double precision performance with 1:3 FP32. However, with the Quadro M6000, NVIDIA has decided to provide only minimal FP64 performance by giving it only 1:32 of FP32 capability and touting the M6000 as the best graphics card rather than the best graphics+compute card like the Quadro K6000. However, software developers will need to optimise their code to take advantage of this technology.AMD GPUsĪMD GPUs perform fairly well for FP64 compared to FP32. Those interested in larger memory capacities for handling more complex CAE problems will likely be interested in NVLink, a technology built into the Quadro GP100, which effectively allows two cards to be connected together for shared GPU resources and memory (up to 32GB). While 16GB is an improvement over the Quadro K6000, the Quadro GP100’s memory footprint is not as big as the Quadro P6000 (24GB), which is tuned for graphics and not double precision floating point operations. With 16 GB of High Bandwidth Memory (HBM2), the GPU can read data at a rate up to 717 GB/sec, which should make it much faster to load complex engineering datasets. In addition to offering significantly bigger compute resources than the Quadro K6000, the Quadro GP100 features bigger and faster memory. The Quadro GP100 features 3,584 CUDA FP 32 cores and 1,792 CUDA FP 32 cores, boasting peak single precision performance (FP 32) of 10.3 TFLOPS and peak double precision performance (FP 64) of 5.2 TFLOPs, which is more than double that of the Quadro K6000. This is the first compute focused workstation GPU that Nvidia has launched since the Kepler-based Quadro K6000 in 2013, with the Maxwell-based Quadro M6000 not tuned for double precision operations that are needed for Finite Element Analysis (FEA) or Computational Fluid Dynamics (CFD). ![]() ![]() Nvidia has launched the Quadro GP100, a ‘Pascal’-based workstation GPU designed specifically for compute tasks, big news for users of simulation software, including Ansys and Abaqus.
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