WHAT IS GPU-ACCELERATED COMPUTING?
GPU-accelerated computing is the use of GPUs in conjunction with CPUs to significantly accelerate deep learning, analysis and engineering applications. Compute-intensive application parts are run on the GPUs and the remaining tasks are processed by the CPUs.
Where are GPU processors used?
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.
GPU vs. CPU performance
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.
Start the first GPU project today
There are three basic approaches to adding GPU acceleration to your applications:
- Dropping in GPU-optimized libraries
- Add compiler hints for automatic parallelization of the code
- Use of extensions for standard languages such as C and Fortran
- Using GPUs with the parallel programming model CUDA is easy to learn
Why sysGen plays a strong role in HPC and Deep Learning Supercomputing:
sysGen has more than 20 years of IT experience in research and development:
- sysGen has a strong partnership with NVIDIA, the leading developer of HPC and Deep Learning hardware and software
- sysGen has a strong partnership with Supermicro. Supermicro's NVIDIA Tesla supported SuperServers® establish Supermicro as the true global leader in High-Performance, Enterprise-Class SuperComputing and GreenIT.
- sysGen has a strong partnership BeeGFS, offering the leading Parallel Cluster File System, developed to deliver high performance and very high fault tolerance
- sysGen offers total Cluster and Deep Learning Solutions, including advanced Management software as turnkey solution
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:
- GPGPU: General Purpose Computation on Graphics Processing Unit refers to the use of a GPU for computing beyond its original purpose. These can be calculations for technical or economic simulations, for example. Parallel algorithms can achieve a huge speed increase compared to CPUs. GPGPU emerged from the shaders of the GPUs. The strength lies in the simultaneous execution of uniform tasks such as coloring pixels or multiplying large matrices. Since the increase in speed of modern processors can no longer be achieved (primarily) by increasing the clock rate, parallelization is an important factor in achieving higher computing power. The GPU benefits are higher computing and memory bandwidths.
GPGPU programming environments:
Progr. Environment, more details
What speaks for our offer:
Why taking our offer, more details