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GPU-accelerated computing is the use of GPUs in conjunction with CPUs to significantly accelerate HPC, deep learning, analysis and engineering applications. Compute-intensive application parts are run on the GPUs and the remaining tasks are processed by the CPUs.
GPU accelerators provide extreme computing power to energy-efficient data centers in research facilities, universities and large, small and medium-sized enterprises around the world. They play a major role in accelerating applications in platforms ranging from artificial intelligence to cars, drones and robots.
A simple way to understand the difference between a GPU and a CPU is to compare how they handle tasks. A CPU consists of a few cores optimized for sequential serial processing. A GPU has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed to handle multiple tasks simultaneously.
Deep Learning is one of the fastest growing territories of the computer industry and probably the fastest growing one in the field. Machine Learning.
Deep Learning uses multi-layered Dynamic Neural Networks (DNNs) to learn a high level of abstraction and representation based on data such as images, sounds or text. In contrast to classical machine learning, where every feature and pattern recognition had to be written by hand, neural networks use algorithms and large amounts of data (big data) to teach themselves. The application areas range from science such as biology or physics, to applications in industry and trade, to the autonomous vehicle.
You can also get our Deep Learning Server ready to use, with pre-installed and pre-configured software.:
High-Performance Computing (HPC) is used for scientific, technical and commercial tasks in the calculation, modeling and simulation of complex systems and the processing of large amounts of data. sysGen has been a successful solution provider for HPC clusters for more than 20 years and had supplied the most powerful HPC clusters with GPU coprocessors in the German-speaking countries.
Today’s groundbreaking scientific discoveries are taking place in high performance computing (HPC) data centers. However, installing and upgrading HPC applications on those shared systems comes with a set of unique challenges. Challenges that decrease accessibility, limit users to old features, and ultimately lower productivity.
sysGen supports the following HPC Cluster types:
There are three basic approaches to adding GPU acceleration to your applications:
sysGen has more than 20 years of IT experience in research and development:
sysGen offers a variety of high-performance, application-optimized GPGPU servers with optimized thermal designs to minimize system power consumption and extremely efficient Platinum power supplies. GPGPUs extend the performance of today's processors by up to 10 times, but differ considerably: