Accelerated computing utilizes specialized hardware to dramatically accelerate work, usually via parallel processing. By offloading repetitive tasks from CPUs, accelerated computing allows them to focus on more challenging jobs more efficiently.
Accelerators typically include graphics processing units (GPUs), application-specific integrated circuits (ASICs) like Tensor Processing Units (TPUs), and field programmable gate arrays. Specialized hardware can improve performance, power efficiency, cost-effectiveness and accuracy.
What is Acceleration?
Acceleration refers to the rate at which an object’s speed increases over time. This could be caused by external forces acting upon it or just simply due to mass moving through space at an increasing speed.
Accelerated processing uses specialized hardware to drastically accelerate work. Accelerated computing uses parallel processing techniques such as graphics processing units (GPUs) to offload tasks that occur frequently from traditional central processor units that work serially. NVIDIA pioneered GPU use to accelerate applications back in 2007 when it launched CUDA programming model that enabled software developers harness the computational power of GPUs for use by applications; since then accelerated computing is widely utilized across technologies from IoT devices to autonomous cars where data-processing needs exceed what traditional CPUs can deliver.
Accelerated computing can best be illustrated by GPU-accelerated systems used to accelerate artificial intelligence (AI) inference workloads. AI models rely on iterative optimization techniques that deconstruct complicated processes into simpler operations with parallel operations – perfectly suited to GPU systems which offer up to 42x better energy efficiency than CPUs for these types of workloads.
Accelerated computing techniques also include employing GPUs for physics simulations, where their parallel processing can help achieve results much more quickly than with traditional CPU-based computation. Likewise, this technology has been applied to image recognition and natural language processing applications to achieve results faster for applications like real-time translations.
Technology also assists IoT computing devices in remote locations to overcome its challenges, with remote devices relying on rapid computation to interpret sensor data quickly and provide actionable intelligence to sensors, combined with AI for more efficient and effective resource use. With its accelerating hardware components, these IoT devices become more robust and responsive than otherwise possible.
Accelerated computing has also proven its worth in fields like deep learning and reinforcement learning, where neural networks require vast quantities of data for training models and inference purposes. Utilizing GPUs’ enhanced performance can significantly decrease training times while freeing up CPU resources for other tasks.
What are the Benefits of Acceleration?
With businesses and consumers collecting more data than ever, it has never been more crucial that companies can efficiently and effectively analyze that information. That is where accelerated computing comes in; using special hardware known as accelerators – such as GPUs or application specific integrated circuits such as Tensor Processing Units (TPUs), or field programmable gate arrays (FPGAs). Accelerating work performed by these devices helps speed up applications that demand high levels of performance.
Accelerated computing offers several immediate advantages over traditional methods. By offloading computationally intensive tasks to specialized hardware devices, these can often provide faster speeds than processors alone – making accelerated computing ideal for processing large datasets or graphics-heavy workloads.
Accelerated computing also brings greater energy efficiency. By employing dedicated devices for demanding tasks, these can process data more efficiently while decreasing power usage – saving companies money while lessening environmental impact.
Accelerated computing can also assist businesses in speeding up development time for certain applications, particularly artificial intelligence (AI). By enabling developers to build and test applications more rapidly, businesses can deploy new services more rapidly – something particularly helpful in the case of AI where new models must first be developed before being used by end users.
AI technology is increasingly being implemented into businesses’ operations to enhance customer experiences and product development; however, with neural networks growing increasingly complex and the volume of data generated increasing exponentially requiring greater processing power for effective performance. Accelerated computing can assist businesses in attaining predictive capabilities that enable more accurate predictions about customers, fraudsters, climate change and even celebrities such as Robert De Niro or Al Pacino’s appearances in future films. However, these solutions may pose unique security risks that must be carefully considered by businesses before adopting them. To minimize these risks, businesses must implement robust cybersecurity protocols before using an accelerated computing solution.
What are the Challenges of Acceleration?
Acceleration does come with its share of challenges. In order to use its technology effectively, new software applications or major modifications to existing ones need to be created or significantly modified in order to take full advantage of acceleration. Furthermore, data moving onto dedicated hardware may present security threats.
Prior to making the decision to deploy accelerated computing in your organization, it is critical that you fully comprehend its impacts and the technology involved. By understanding its effects and potential impact, you can ensure your data security as well as system performance will meet expectations.
Specialized hardware accelerators – like those found in graphics processing units (GPUs) or application-specific integrated circuits (ASICs) – typically provide much faster real-world execution times than traditional CPUs. They do this by offloading parallelizable computationally intensive portions of an application onto these specialized accelerators while the rest of its code continues running on its original source CPUs.
Accelerated computing solutions may offer significant performance boosts, yet are more energy intensive than CPUs. Businesses considering switching should ensure they carefully assess the total energy consumption of their systems before making the change.
Accelerated hardware must be updated frequently as software changes, which requires considerable effort and can increase costs of adoption. As more enterprises turn to AI technologies for AI endeavors, enterprises must find solutions that minimize this complexity.
Accelerated computing has become an indispensable feature of modern life. From protecting against credit card fraud while you shop online and smooth movie streaming to meeting transaction processing needs in financial trading firms and automotive engineering shops to create advanced driver assistance features in cars – and video game developers using accelerated computing for creating stunning graphics and immersive experiences – accelerated computing is everywhere today.
As the world shifts towards being data-centric, demand for processing massive amounts of information will only continue to increase exponentially. Accelerated computing solutions – like GPU-based accelerated computing – become more important to helping organizations address industry challenges. Solutions like GPU-accelerated computing can bridge the gap between processing needs and traditional CPU capabilities by dramatically decreasing cycle times while freeing up more compute resources for companies looking to lead in their respective industries.
What are the Solutions for Acceleration?
Accelerated computing solutions typically involve using special-purpose hardware processors known as accelerators to offload computationally-intensive tasks from a general-purpose CPU that works serially, thus providing faster real-world execution times by taking advantage of data parallelism offered by accelerators while freeing up CPU resources to handle other parts of an application.
GPUs have long been utilized by video game engineers as an expedient way of speeding up image rendering in video games. Their primary function in this task is matrix multiplication – an intensive task which quickly overwhelms any general-purpose CPU. Engineers solved this issue through hardware acceleration by creating GPUs specialized specifically for this job.
GPUs are also ideal for accelerating AI applications as they can be programmed to execute tasks simultaneously and learn in a way that reduces processing times. Switching all servers running AI worldwide to GPU-accelerated systems would save 10 trillion watt-hours annually — equivalent to what 1.4 million homes consume annually!
Application-specific integrated circuits (ASIC) offer another means of accelerating computing by offering dedicated chips designed specifically to perform one operation very well, outperforming general-purpose CPUs and GPUs; often used for deep learning and other AI tasks that need specific, low-latency acceleration.
FPGAs, or field-programmable gate arrays, are another accelerated computing solution that offers greater customization to suit specific applications. After being manufactured, these devices can be reconfigured after purchase to adapt their architecture specifically to suit the application at hand; ideal for programs with frequent updates or with specific requirements that cannot be fulfilled with fixed architecture devices like GPUs or CPUs.
Accelerated computing is at the core of modern applications that are revolutionizing every industry, from AI-powered business forecasting and autonomous vehicles, to advanced visualization and medical diagnosis. Businesses must understand and leverage accelerated computing as soon as possible in order to meet customer demands and remain relevant in today’s marketplace.
