Kepler Cores

Cores than the SM of Fermi GPUs, yielding a throughput improvement of 2-3x per clock.4 Furthermore, GK110 has increased memory bandwidth over Fermi and GK104. Kepler has increased the maximum number of simultaneous blocks per multiprocessor from 8 to 16. As a result, kernels having their occupancy limited

Portrait of Johannes Kepler, eponym of architecture. Kepler is the codename for a GPU microarchitecture developed by Nvidia, first introduced at retail in April 2012, 1 as the successor to the Fermi microarchitecture. Kepler was Nvidia's first microarchitecture to focus on energy efficiency.Most GeForce 600 series, most GeForce 700 series, and some GeForce 800M series GPUs were based on

All the Nvidia GPUs belonging to Tesla, Fermi, Kepler, Maxwell, Pascal, Volta, Turing, and Ampere have CUDA cores. But the same can not be said about the Tensor cores or Ray-Tracing cores. The first Fermi GPUs featured up to 512 CUDA cores, each organized as 16 Streaming Multiprocessors of 32 cores each.

CUDA Cores 2688 2496 Peak Double Precision Peak DGEMM 1.32 TF 1.22 TF 1.17 TF 1.10 TF Peak Single Precision Peak SGEMM 3.95 TF 2.90 TF 3.52 TF 2.61 TF Memory Bandwidth 250 GBs 208 GBs Memory size.43 TFLOPS 6 GB 5 GB Total Board Power 235W 225W Tesla K20 Family 3x Faster Than Fermi 0 0.25 0.5 0.75 1 1.25 Xeon E5-2690 Tesla M2090 Tesla K20X PS

Each SM contains 192 CUDA cores up from 32 cores in Fermi. PCI-Express generation 3.0 doubles data transfer rates between the host and the GPU. GPU Boost increases the clock speed of all CUDA cores, providing a 30 performance boost for many common applications. Each SM contains more than twice as many registers with another 2X on Tesla K80.

32 CORES 192 CORES SMX KEPLER 3X PERFWATT. to more easily take advantage of the immense parallel processing capability of the GPU. To this end, the new Dynamic Parallelism feature enables the Kepler GK110 GPU to dynamically spawn new threads by adapting to the data without

The reason NVIDIA has managed to squeeze so many cores onto one die is because Kepler is the firm's first chip produced on a smaller 28nm process. Despite tripling the number of cores, the phsyical die size is about two thirds smaller than Fermi, and has just 500 million more transistors 3.5billion compared to 3billion.

Comprising 7.1 billion transistors, the Kepler GK110210 architecture incorporates many new innovative features focused on compute performance. Kepler GK110 and GK210 are designed to be a parallel processing powerhouses for Tesla and the HPC market. Both Kepler GK110 and 210 provide over 1 TFlop of double precision throughput with greater

Kepler's SMX is distantly related to Fermi's SMs, but is much larger and prioritizes power efficiency. Fermi ran the execution units at twice the GPU core clock to maximize compute power within area constraints, but that resulted in high power consumption. Therefore 96 quotcuda coresquot on a GK104 chip are equivalent to 48 on a GF104

Kepler, our priority was performance per watt. While we made many optimizations that benefitted both area and power, we chose to optimize for power even at the expense of some added area cost, with a larger number of processing cores running at the lower, less powerhungry GPU clock.