What is GPU Computing?
Graphics processing unit (GPU) computing is the process of offloading processing needs from a central processing unit (CPU) in order to accomplish smoother rendering or multitasking with code via parallel computing.
How is GPU computing related to deep learning and AI?
GPU computing has become the key to optimizing deep learning, accelerating time to value (TTV), increasing processing speed during coding, enhancing data management, content creation, and product engineering, and delivering comprehensive insight into data analytics.
This multifaceted and beneficial process happens through parallel computing. When CPUs become overwhelmed with processing massive volumes of data (i.e., Big Data), the GPU steps in and separates complex problems into millions of tasks, making it easier to find solutions all at once. The GPU runs various levels of tasks consecutively, which frees up the normal processing capabilities of the CPU and protects the integrity of both systems by allocating specific workloads to the most efficient processor for the job. Both the CPU and GPU can work together in an artificial intelligence (AI) ecosystem, supporting problem solving interchangeably.
How are GPUs and CPUs related?
GPUs get the credit for leading the charge in supercomputing. In situations when graphics or content must be rendered at high speeds, GPUs are essential. Utilizing GPU computing benefits the internal CPU, allowing for processing and rendering graphics at an accelerated rate.
This alliance between graphics processing units and central processing units promotes a smoother processing system, achieving utilization that could not be achieved by a CPU on its own. While CPUs do have much higher processing speeds, GPUs have unmatched processing capabilities thanks to parallelism.
What are the benefits of GPU computing?
Acting as a companion processor to CPUs, GPUs exponentially boost the speed and processing capabilities of a system. GPUs perform computing applications concerning technical and scientific data in an accelerated way, adding efficiency when they are integrated alongside CPUs.
Another benefit to using GPUs is that they lessen the burden on the CPU by processing repetitive data in smaller chunks across several processors and enabling computing to proceed uninhibited by the limitless number of problems that it is tasked with solving.
In addition to processing power, GPUs extend memory bandwidth. Working hundreds of times faster than CPUs, GPUs make the automation and intelligence of machine learning (ML) and Big Data analysis possible, as they process massive amounts of data via neural networks. The AI then learns deeply complex tasks that no data scientist would have the language to teach or translate.
Additional benefits include but are not limited to:
- Superior processing power
- Exponentially greater memory storage/bandwidth
- Robust data analysis and analysis of AI and ML
- Rapid advances in gaming and graphics
- Easy integration into data centers
How does GPU computing work?
IT’s focus has shifted to reflect and support the compute demands of AI and data science. This work is done by GPUs. Applications that are being run on CPUs are accelerated using GPU compute, which optimizes performance and workload capacity.
GPU computing enables applications to run with extreme efficiency by offloading series of computational scientific and technical tasks from the CPU. GPUs process thousands of tasks in seconds through their hundreds of cores via parallel processing. Parallel processing denotes a function where data sets are funneled into a GPU’s processing cores and are all solved for simultaneously. Performance is increased as the GPU crunches and translates data while the CPU is running the remaining applications.
Insights from data analytics lead the way in problem solving and increased functionality with the use of GPU compute. GPU’s ability to quickly process and sort massive amounts of data allows for industry leaders to quickly and accurately access the insights into their data and innovate accordingly.
GPU Computing and HPE
HPE is a reliable partner to enterprise organizations through compute infrastructure offerings in both hardware and software. Offering state-of-the-art solutions, HPE supports IT through on-premises, co-location, and edge-to-cloud compute systems. There are a variety of pre-set configurations to choose from. Whether for a Big Data analytics solution, general-use infrastructure, or an optimized modular infrastructure, HPE lends support when companies and institutions need it most.
With HPE Proliant comes a new intelligent compute foundation for enterprise use, delivering enhanced capabilities in security, automation, and compute processing power. Specifically designed for hybrid cloud use, Proliant servers accelerate AI and consolidate management for IT.
HPE Apollo systems lend support through providing supercomputing to data centers and AI applications. HPE Apollo’s capabilities build infrastructures that support data-intensive workloads and promote innovation by intentional access and analysis of the most complex problems within data.
Structured as a pay-per-use framework, HPE compute solutions have the capacity to grow as needed; this covers the normal fluctuations and the unpredictable increases in demand. Not only does rapid growth determine capacity needs, but unexpected issues pull on resources which further affects compute capacity and efficiency. HPE offers on-demand scaling options with a buffer included to lend full support.
Resources for virtualized enterprise compute solutions are discoverable with HPE GreenLake. With the HPE GreenLake experience, you can leverage all your security and oversight needs while maintaining control over costs with its pay-per-use model.