Processor
| Processor frequency |
1150 MHz |
| Graphics processor family |
NVIDIA |
| CUDA |
Y |
Memory
| Graphics adapter memory type |
GDDR5 |
| Memory bandwidth (max) |
144 GB/s |
| Memory bus |
384 bit |
| Memory clock speed |
1500 MHz |
Ports & interfaces
| Interface type |
PCI Express x16 |
Performance
| PhysX |
N |
| TV tuner integrated |
N |
Tesla C2050, 3GB GDDR5, 1.5GHz, PCIe x16
- GPUs powered by the Fermi-generation of the CUDA architecture
Delivers cluster performance at 1/10th the cost and 1/20th the power of CPU-only systems based on the latest quad core CPUs.
- 448 CUDA Cores
Delivers up to 515 Gigaflops of double-precision peak performance in each GPU, enabling a single workstation to deliver a Teraflop or more of performance. Single precision peak performance is over a Teraflop per GPU.
- ECC Memory
Meets a critical requirement for computing accuracy and reliability for workstations. Offers protection of data in memory to enhance data integrity and reliability for applications. Register files, L1/L2 caches, shared memory, and DRAM all are ECC protected.
-Desktop Cluster Performance
Solves large-scale problems faster than a small server cluster on a single workstation with multiple GPUs.
- 3GB of GDDR5 memory per GPU
Maximizes performance and reduces data transfers by keeping larger data sets in local memory that is attached directly to the GPU.
- NVIDIA Parallel DataCache™
Accelerates algorithms such as physics solvers, ray-tracing, and sparse matrix multiplication where data addresses are not known beforehand. This includes a configurable L1 cache per Streaming Multiprocessor block and a unified L2 cache for all of the processor cores.
- NVIDIA GigaThread™ Engine
Maximizes the throughput by faster context switching that is 10X faster than previous architecture, concurrent kernel execution, and improved thread block scheduling.
- Asynchronous Transfer
Turbocharges system performance by transferring data over the PCIe bus while the computing cores are crunching other data. Even applications with heavy data-transfer requirements, such as seismic processing, can maximize the computing efficiency by transferring data to local memory before it is needed.
- CUDA programming environment with broad support of programming languages and APIs
Choose C, C++, OpenCL, DirectCompute, or Fortran to express application parallelism and take advantage of the “Fermi” GPU’s innovative architecture. NVIDIA Parallel Nsight™ tool is available for Microsoft Visual Studio developers.
- High Speed , PCIe Gen 2.0 Data Transfer
Maximizes bandwidth between the host system and the Tesla processors. Enables Tesla systems to work with virtually any PCIe-compliant host system with an open PCIe x16 slot.
