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The Tesla Kepler K20 Personal Supercomputing GPU for Workstations from nVIDIA turns standard PCs and workstations into personal supercomputers. Powered by CUDA, the pervasive parallel-computing model, Tesla GPU accelerators for workstations deliver cluster-level performance right at your desk.
The supercomputing GPU is ideal for high-performance computing workloads, including seismic processing, biochemistry simulations, weather and climate modeling, image, video and signal processing, computational finance, computational physics, CAE, CFD and data analytics.
SMX (Streaming Multiprocessor)
Tesla K20 features SMX technology which delivers up to three times more performance per watt compared to the SM in Fermi. It also delivers one petaflop of computing in just ten server racks.
Dynamic Parallelism enables GPU threads to automatically spawn new threads. By adapting to the data without going back to the CPU, it greatly simplifies parallel programming. Additionally, this technology enables GPU acceleration of a broader set of popular algorithms like adaptive mesh refinement (AMR), fast multi-pole method (FMM) and multi-grid methods.
Hyper-Q technology enables multiple CPU cores to simultaneously utilize the CUDA cores on a single Kepler GPU. This dramatically increases GPU utilization, slashes CPU idle times and advances programmability-ideal for cluster applications that use MPI.
ECC Memory Error Protection
ECC memory meets the critical requirement for computing accuracy and reliability in data centers and supercomputing centers. 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 administrators can now manage 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 in advance.
Asynchronous Transfer with Dual DMA Engines
Turbo-charges system performance by transferring data over the PCIe bus while the computing cores are executing other data.
Flexible Programming Environment with Broad Support for 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.
GPU Computing Applications
GPU accelerators are ideal for the most aggressive high-performance computing workloads including climate and weather modeling, CFD, CAE, computational physics, biochemistry
simulations, and computational finance.
In the Box
NVIDIA Tesla Kepler K20 Personal Supercomputing GPU for Workstations