Fastest , Most Efficient HPC Architecture Ever Built
NVIDIA® Kepler GPU Computing Accelerators are the world's fastest and most efficient high performance computing (HPC) companion processors. Based on the Kepler compute architecture, which is 3 times higher performance per watt than the previous "Fermi" compute architecture1, the Tesla Kepler GPU Computing Accelerators make hybrid computing dramatically easier, and applicable to a broader set of computing applications. NVIDIA Tesla GPUs deliver the best performance and power efficiency for seismic processing, biochemistry simulations, weather and climate modeling, image, video and signal processing, computational finance, computational physics, CAE, CFD, and data analytics.
The innovative design of the Kepler compute architecture includes:
- SMX (streaming multiprocessor) design that delivers up to 3x more performance per watt compared to the SM in Fermi. It also delivers 1 petaflop of computing in just 10 server racks.
- Dynamic Parallelism capability that enables GPU threads to automatically spawn new threads. By adapting to the data without going back to the CPU, it greatly simplifies parallel programming and enables GPU acceleration of a broader set of popular algorithms, like adaptive mesh refinement (AMR), fast multipole method (FMM), and multigrid methods.
- Hyper-Q feature that enables multiple CPU cores to simultaneously utilize the CUDA cores on a single Kepler GPU, dramatically increasing GPU utilization, slashing CPU idle times, and advancing programmability. Ideal for cluster applications that use MPI.
Tesla K20 GPU Computing
Accelerator
Designed for double
precision applications and the broader
supercomputing market, the Tesla
K20 delivers 3x the double precision
performance compared to the previous
generation Fermi-based Tesla M2090,
in the same power envelope. Tesla K20
features a single GK110 Kepler GPU
that includes the Dynamic Parallelism and Hyper-Q features.
With more than one teraflop peak double precision performance,
the Tesla K20 is ideal for a wide range of high performance
computing workloads including climate and weather modeling,
CFD, CAE, computational physics, biochemistry simulations, and
computational finance.
TESLA GPU COMPUTING ACCELERATOR COMMON FEATURES
| ECC Memory Error Protection | Meets a critical requirement for computing accuracy and reliability in datacenters and supercomputing centers. External DRAM is ECC protected in Tesla K10 and both external and internal memories are ECC protected in Tesla K20. |
| System Monitoring Features | Integrates the GPU subsystem with the host system's monitoring and management capabilities such as IPMI or oEM-proprietary tools. IT staff can thus manage the GPU processors in the computing system using widely used cluster/grid management solutions. |
| L1 and L2 caches | Accelerates algorithms such as physics solvers, ray-tracing, and sparse matrix multiplication where data addresses are not known beforehand |
| Asynchronous Transfer with dual DMA engines | Turbocharges system performance by transferring data over the PCIe bus while the computing cores are crunching other data. |
| Flexible programming environment with broad support of programming languages and APIs | Choose openACC, CUDA toolkits for C, C++, or Fortran to express application parallelism and take advantage of the innovative Kepler architecture. |
| Chip | GK110 |
| Processor clock | 706 MHz |
| Memory clock | 2.6 GHz |
| Memory size | 5 G |
| Memory I/O | 320-bit GDDR5 |
| Memory configuration | 20 pieces of 128M x 16 GDDR5 SDRAM |
| Display connectors | None |
| Power connector | ~225W |
| Thermal cooling solution | Active fan sink |