Tesla C2070 / 2075 GPU Computing Processor
The NVIDIA Tesla™ C2070 and C2075 Computing Processors fuel the transition to parallel computing and bring the performance of a small cluster to the desktop. Based on the next-generation CUDA architecture codenamed “Fermi”, the 20-series family of Tesla GPUs support many “must have” features for technical and enterprise computing including C++ support, ECC memory for uncompromised accuracy and scalability, and a 7X increase in double precision performance compared Tesla 10-series GPUs. The Tesla™ C2070 and C2075 GPUs are designed to redefine high performance computing and make supercomputing available to everyone. Compared to the latest quad-core CPUs, Tesla C2070 and C2075 Computing Processors deliver equivalent supercomputing performance at 1/10th the cost and 1/20th the power consumption.
Tesla C2070/C2075 GPU are now available with DALCO workstations and servers.
Features
| 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 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 | Solve large-scale problems faster than a small server cluster on a single workstation with multiple GPUs. |
| Up to 6GB 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™ Technology | 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 capability | 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. |
Technical Specifications
| Form Factor | 9.75" x 4.376", Dual Slot |
|---|---|
| # of Tesla GPUs | 1 |
| # of CUDA Cores | 448 |
| Frequency of CUDA Cores | 1.15 Ghz |
| Single Precision Floating Point Performance (peak) | 1.03 TFlops |
| Double Precision Floating Point Performance (peak) | 515 GFlops |
| Total Dedicated Memory* | Tesla C2070 6GB GDDR5* Tesla C2075 6GB GDDR5* |
| Memory Speed | 1.55 GHz |
| Memory Interface | 384-bit |
| Memory Bandwidth | 148 GB/sec |
| Power Consumption | 225W TDP |
| System Interface | PCIe x16 Gen2 |
| Thermal Solution | Active Fan Sink |
| System Interface | CUDA C/C++/Fortran, OpenCL, DirectCompute Toolkits. NVIDIA Parallel Nsight™ for Visual Studio |
* With ECC on, a portion of the dedicated memory is used for ECC bits, so the available user memory is reduced by 12.5%. (e.g. 3 GB total memory yields 2.625 GB of user available memory.)
