NVIDIA Tesla K80 GPU Accelerator
The world’s fastest accelerator for data analytics and scientific computing
Introducing the NVIDIA® Tesla® K80 GPU Accelerator, the newest addition to the Tesla accelerated-computing platform. The world’s fastest accelerator for the most complex high-performance data analytics and scientific computing applications, the Tesla K80 GPU has 2x the memory and is 80% faster than its predecessor. Plus, it outperforms CPUs by up to 10x on hundreds of applications. SCAN supply our customers with industry leading graphics from manufacturer PNY Technologies with their amazing warranty and reliabillity.
Accelerate your most demanding high-performance data analytics and scientific computing applications with the NVIDIA Tesla Accelerated-Computing Platform. Tesla GPU Accelerators are built on the NVIDIA Kepler™ compute architecture and powered by CUDA,® the world’s most pervasive parallelcomputing model. This makes them ideal for delivering record acceleration and compute performance efficiency for applications in fields including:
- > Machine Learning and Data Analytics
- > Seismic Processing
- > Computational Biology and Chemistry
- > Weather and Climate Modeling
- > Image, Video, and Signal Processing
- > Computational Finance/Physics
- > CAE and CFD
Tesla Accelerated Computing Platform
The Kepler-based Tesla family of GPUs is part of the innovative Tesla Accelerated Computing Platform. As the leading platform for accelerating data analytics and scientific computing, it combines the world’s fastest GPU accelerators, the widely used CUDA parallel computing model, and a comprehensive ecosystem of software developers, software vendors, and datacenter system OEMs.
Tesla K80 GPU Accelerator
This accelerator is designed for the most demanding computational tasks, combining 24 GB of memory with blazing-fast memory bandwidth and leading compute performance for single and double precision workloads. Equipped with the latest NVIDIA GPU Boost™ technology, the Tesla K80 intelligently monitors GPU usage to maximize throughput and outperforms CPUs by up to 10x.2.
|TECHNICAL SPECIFICATIONS||Tesla K40||Tesla K80|
|Peak double-precision floating point performance (board)||1.43 Tflops||1.87 Tflops|
|Peak single-precision floating point performance (board)||4.29 Tflops||5.6 Tflops|
|GPU||1 x GK110B||2 x GK210|
|CUDA cores||2,880 4,992||4,992|
|Memory size per board (GDDR5)||12 GB 24 GB||24 GB|
|Memory bandwidth for board (ECC off)||288 Gbytes/sec||480 Gbytes/sec|
|Architecture features||SMX, Dynamic Parallelism, Hyper-Q|
|System||System Servers and workstations||Servers|
|FEATURES||Tesla K40||Tesla K80|
Enables GPU threads to automatically spawn new threads. By adapting to the data without going back to the GPU, this greatly simplifies parallel programming.
Allows multiple CPU cores to simultaneously use the CUDA cores on a single or multiple Kepler-based GPUs. This dramatically increases GPU utilization, simplifies programming, and slashes CPU idle times.
Integrates the GPU subsystem with the host system’s monitoring and management capabilities, such as IPMI or OEM-proprietary tools. IT staff can now 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
|Memory Error Protection
Meets a critical requirement for computing accuracy and reliability in data centers and supercomputing centers. Both external and internal memories are ECC protected in the Tesla K80 and K40.
|Asynchronous Transfer with Dual DMA Engines
Turbocharges system performance by transferring data over the PCIe bus while the computing cores are crunching other data
Enables the end-user to convert power headroom to higher clocks and achieve even greater acceleration for various HPC workloads
|Dynamically scales GPU clocks for maximum application performance and improved energy efficiency|
|Flexible Programming Environment with Broad Support of Programming Language and APIs
Offers the freedom to choose OpenACC, CUDA toolkits for C, C++, or Fortran to express application parallelism and take advantage of the innovative Kepler architecture
|2x Shared Memory and 2x Register File
Increases effective throughput and bandwidth with 2x shared memory and 2x register file compared to the K40
Increases data center energy efficiency by powering down idle GPUs when running legacy nonaccelerated workloads