NVIDIA GPU – One Platform. Unlimited Data Center Acceleration.
NVIDIA GPU – Accelerating scientific discovery, visualizing big data for insights, and providing smart services to consumers are everyday challenges for researchers and engineers. Solving these challenges takes increasingly complex and precise simulations, the processing of tremendous amounts of data, or training sophisticated deep learning networks. These workloads also require accelerating data centers to meet the growing demand for exponential computing.
NVIDIA Tesla is the world’s leading platform for accelerated data centers, deployed by some of the world’s largest supercomputing
centers and enterprises. It combines GPU accelerators, accelerated computing systems, interconnect technologies, development tools, GPU applications and Compilers, like PGI to enable faster scientific discoveries and big data insights.
At the heart of the Tesla platform are the massively parallel GPU accelerators that provide dramatically higher throughput for compute‑intensive workloads—without increasing the power budget and physical footprint of data centers.
Nvidia Tesla V100
The Most Advanced Data Center GPU Ever Built.
NVIDIA® Tesla® V100 is the world’s most advanced data center GPU ever built to accelerate AI, HPC, and graphics. Powered by NVIDIA Volta™, the latest GPU architecture, Tesla V100 offers the performance of 100 CPUs in a single GPU—enabling data scientists, researchers, and engineers to tackle challenges that were once impossible.Read more about Nvidia Tesla V100 Volta GPU
Three Reasons to Upgrade to the NVIDIA Tesla V100
Be Prepared for the AI Revolution
NVIDIA Tesla V100 is the computational engine driving the AI revolution and enabling HPC breakthroughs. For example, researchers at the University of Florida and the University of North Carolina leveraged GPU deep learning to develop ANAKIN-ME (ANI) to reproduce molecular energy surfaces at extremely high (DFT) accuracy and 1-10/millionths of the cost of current computational methods.
Boost Data Center Productivity & Throughput
Data center managers all face the same challenge: how to meet the demand for computing resources that often exceed available cycles in the system.
NVIDIA Tesla V100 dramatically boosts the throughput of your data center with fewer nodes, completing more jobs and improving data center efficiency.
A single server node with V100 GPUs can replace up to 50 CPU nodes. For example, for HOOMD-Blue, a single node with four V100’s will do the work of 43 dual-socket CPU nodes while for MILC a single V100 node can replace 14 CPU nodes. With lower networking, power, and rack space overheads, accelerated nodes provide higher application throughput at substantially reduced costs.
Top Applications are GPU-Accelerated
Over 450 HPC applications are already GPU-optimized in a wide range of areas including quantum chemistry, molecular dynamics, climate and weather, and more.
In fact, an independent study by Intersect360 Research shows that 70% of the most popular HPC applications, including 10 of the top 10 have built-in support for GPUs.
With the most popular HPC applications and all deep learning frameworks GPU-accelerated, every HPC customer would see most of their data center workload benefit from GPU-accelerated computing.
Choose the Right NVIDIA Tesla Solution for You
The Exponential Growth of GPU Computing
For more than two decades, NVIDIA has pioneered visual computing, the art and science of computer graphics. With a singular focus on this field, NVIDIA GPUs offers specialized platforms for the gaming, professional visualization, data center, GPU server and automotive markets. NVIDIA’s work is at the center of the most consequential mega-trends in GPU cluster technology — virtual reality, artificial intelligence and self-driving cars.
GPU servers have become an essential part in the computational research world. From bioinformatics to weather modeling, GPUs have offered over 70x speed up on researcher’s code. With hundreds of applications already accelerated by these cards, check to see if your favorite applications are on the GPU applications list.
Tools for GPU Computing.
GPU Accelerated Libraries
There are a handful of GPU accelerated libraries that developers can use to speed up applications using GPUs. Many of them are NVIDIA CUDA libraries (such as cuBLAS and CUDA Math Library), but there are others such as IMSL Fortran libraries and HiPLAR (High Performance Linear Algebra in R). These libraries can be linked to replace standard libraries that are commonly used in non-GPU-Accelerated computing.
NVIDIA has created an entire toolkit devoted to computing on their CUDA-enabled GPUs. The CUDA toolkit, which includes the CUDA libraries, are the core of many GPU-Accelerated programs. CUDA is one of the most widely used toolkits in the GPGPU world today.
NVIDIA Deep Learning SDK
In today’s world, Deep Learning is becoming essential in many segments of the industry. For instance, Deep Learning is key in voice and image recognition where the machine must learn while gaining input. Writing algorithms for machines to learn from data is a difficult task, but NVIDIA has written a Deep Learning SDK to provide the tools necessary to help design code to run on GPUs.
The OpenACC Directives can be a powerful tool in porting a user’s application to run on GPU servers. There are two key features to OpenACC and that are that is it easy, and portable. Applications that use OpenACC can not only run on NVIDIA GPUs, but it can run on other GPUs and CPUs as well.
NVIDIA now offers the TESLA P100 card that boasts 5.3 TeraFLOPS of double-precision performance for your code. Until these become more available, NVIDIA still has other GPUs such as K40 and K80 cards. If your applications depend on single precision code, the M40 and M60 cards are the right cards for you.
Tesla GPU Accelerators for HPC Servers
Accelerate your most demanding HPC, hyperscale and enterprise data center workloads with NVIDIA Tesla GPU accelerators. Scientists can now crunch through petabytes of data up to 10x faster than with CPUs in applications ranging from energy exploration to deep learning.