Accelerated computing, with its unprecedented processing power, has taken a central role in cloud infrastructure, as it helps manage vast amounts of information in data centers more efficiently and effectively. Furthermore, accelerated computing provides the necessary computational power and memory to train and implement advanced generative AI models, such as GPT-4, more efficiently. This capability enables faster training times, the handling of large datasets, and the development of increasingly complex models.

Accelerated computing utilizes specialized hardware like GPUs, ASICs, TPUs, and FPGAs to execute computations more efficiently than CPUs, enhancing speed and performance. It is especially beneficial for tasks that can be parallelized, such as in high-performance computing, deep learning, ML, and AI.

The world of accelerated computing is vast and ever-evolving, packed with diverse hardware and software solutions such as GPUs, ASICs, TPUs, FPGAs, CUDA, OpenCL, and various networking technologies. Dgtl Infra delves into the practical implementations of these solutions in the cloud with major providers like Amazon Web Services (AWS), Google Cloud, and Microsoft Azure. We also explore their impact on AI-driven applications, including generative AI and data centers. Continue reading to understand the massive implications that accelerated computing can have in reshaping our technological future.

What is Accelerated Computing?

Accelerated computing refers to the use of specialized hardware to perform certain types of computations more efficiently than is possible with general-purpose central processing units (CPUs) alone. This concept leverages the power of devices such as graphics processing units (GPUs), application-specific integrated circuits (ASICs) – including tensor processing units (TPUs) – and field-programmable gate arrays (FPGAs) to perform calculations at significantly higher speeds, thereby accelerating the process.

These accelerators are particularly well-suited for tasks that can be broken down into smaller, parallel tasks, such as those often found in high-performance computing (HPC), deep learning, machine learning (ML), artificial intelligence (AI), and big data analytics. By offloading certain types of workloads to these specialized hardware devices, accelerated computing greatly enhances the performance and efficiency of these systems.

Importance of Accelerated Computing

The end of Moore’s Law, which posits that computing power roughly doubles every two years at constant cost, has marked a slowdown in CPU performance growth. This has sparked a shift towards accelerated computing and raised questions about the long-term viability of the current $1 trillion market for CPU-only servers. With increasing demand for more powerful applications and systems, traditional CPU methods struggle to compete with accelerated computing, which offers faster and more cost-efficient performance upgrades.

Accelerated computing is vital due to its ability to process massive volumes of data efficiently, thereby driving advancements in ML, AI, real-time analytics, and scientific research. Its increasing influence in graphics, gaming, edge computing, and cloud computing forms the backbone of the digital infrastructure, like data centers, necessary for our increasingly data-driven and interconnected world.

Accelerated Computing Solutions – Hardware, Software, and Networking

Accelerated computing solutions involve a combination of hardware, software, and networking. These solutions are specifically designed to enhance the speed and efficiency of complex computational tasks, primarily by performing them in parallel.

Hardware Accelerators

Hardware accelerators are fundamental to accelerated computing, significantly outperforming what traditional central processing units (CPUs) alone can achieve. These accelerators include graphics processing units (GPUs), application-specific integrated circuits (ASICs), and field-programmable gate arrays (FPGAs).

PurposeGeneral-purpose computingSpecific tasks only; fully customizedProgrammable for various tasks
SpeedHighHighestLower than GPUs and ASICs
FlexibilityLowLowest (single task)High (fully reconfigurable)
Cost>$10,000$5,000 or below;
Depends on volume
$3,000 to $10,000
Development TimeLowHighest (requires custom design)Moderate (requires programming)
Power EfficiencyModerateHighestModerate to high
Software and APIsCUDA, OpenCLCustomized for individual processorsVerilog, VHDL, OpenCL, HLS
Typical Use CasesGaming, AI/ML, graphicsCryptocurrency mining, specific computationsPrototyping, adaptable computations
Graphics Processing Units (GPUs)

Graphics processing units (GPUs) are specialized processors widely used for a variety of computationally intensive tasks. They are especially proficient at performing many complex calculations simultaneously, making them well-suited for tasks such as high-performance computing (HPC) and training neural networks in machine learning.

In the context of application, the term general-purpose graphics processing unit (GPGPU) is often used to describe the use of a GPU for computations that are traditionally handled by the central processing unit (CPU), in fields beyond graphics rendering.

NVIDIA leads the market in GPUs used for data centers and compute-intensive tasks such as machine learning and AI. The company’s primary GPU architectures for data centers include Hopper (H100) and Ampere (A100). Notably, the H100 GPUs are well-suited for accelerating applications involving large language models (LLMs), deep recommender systems, genomics, and complex digital twins.

Application-Specific Integrated Circuits (ASICs)

Application-specific integrated circuits (ASICs) are custom chips designed to perform a specific task, unlike general-purpose CPUs which are engineered to handle a wide variety of applications. Given that ASICs are custom-tailored for a particular function, they execute that task more efficiently than a general-purpose processor. This results in advantages in terms of speed, power consumption, and overall performance.

Neural processing unit (NPU) and deep learning processor (DLP) are terms often used to refer to specific types of ASICs, that are designed to accelerate AI workloads. A prominent example of an ASIC in the context of accelerated computing is Google’s tensor processing unit (TPU). TPUs are designed specifically for accelerating machine learning workloads. They are used extensively in Google’s data centers for tasks such as language translation, voice recognition in Google Assistant, and programmatic advertisement ranking workloads.

Field-Programmable Gate Arrays (FPGAs)

Field-programmable gate arrays (FPGAs) are semiconductor integrated circuits that can be reprogrammed to perform specific tasks more efficiently than general-purpose CPUs. Unlike the fixed architectures found in ASICs, GPUs, and CPUs, FPGA hardware includes configurable logic blocks and programmable interconnects. This allows for functionality updates even after the chip has been shipped and deployed.

FPGAs are growing increasingly popular in data centers for high-performance computing (HPC) and AI/ML acceleration, given their flexibility and parallel computing capabilities. However, they are slower than GPUs and custom ASIC solutions, and their software ecosystem is not as developed. The sluggish adoption of FPGAs for AI workloads is primarily attributable to their programming complexity, which has resulted in a limited number of specialized engineers.

Software and APIs

Accelerated computing utilizes application programming interfaces (APIs) and programming models such as CUDA and OpenCL to interface with both software and hardware accelerators. This optimizes data flow for better performance, power efficiency, cost-effectiveness, and accuracy. APIs and programming models enable developers to write code that runs on GPUs and utilize software libraries for efficient algorithm implementation.

CUDA (Compute Unified Device Architecture)

CUDA (Compute Unified Device Architecture) is a proprietary parallel computing platform and application programming interface (API) model developed by NVIDIA. It enables the use of NVIDIA GPUs for general-purpose processing, resulting in a significant acceleration of computational tasks. The platform includes deep learning libraries like cuDNN, TensorRT, and DeepStream to enhance AI training and inference tasks.

Since its introduction in 2006, CUDA has been downloaded 40 million times, boasting a user base of 4 million developers worldwide. This wide adoption provides new applications developed using the CUDA platform with a substantial developer community. As CUDA is a proprietary platform with a vast developer base, NVIDIA holds a significant advantage in the data center hardware and software market. Consequently, smaller vendors often find themselves at a disadvantage as they must rely on the OpenCL platform, which does not deliver performance on-par with CUDA.

OpenCL (Open Computing Language)

OpenCL (Open Computing Language) is an open-source platform designed for parallel computing. It supports a wide range of computing hardware, including CPUs, GPUs, FPGAs, and other types of processors. This broad compatibility enables developers to harness the power of these diverse hardware components, accelerating computing tasks. A particularly notable feature of OpenCL is its portability across different hardware types.


Networking plays a crucial role in accelerated computing as it facilitates communication among tens of thousands of processing units, such as GPUs, as well as memory and storage devices. Various networking technologies are utilized to enable communication between these computing devices and the rest of the system, and to share data among multiple devices within a network. These include:

  • PCI Express (PCIe): this high-speed serial computer expansion bus standard provides a direct connection between the computing devices and the CPU/memory. In accelerated computing, PCIe is commonly used to connect GPUs or other accelerators to the main system
  • NVLink: NVIDIA’s proprietary high-bandwidth, energy-efficient interconnect technology provides significantly higher bandwidth than PCIe. It is designed to facilitate more efficient data sharing between GPUs, and also between GPUs and CPUs
  • Infinity Fabric: AMD’s proprietary interconnect technology is used to connect various components within their chips, including CPUs, GPUs, and memory
  • Compute Express Link (CXL): an open interconnect standard, CXL helps in reducing latency and increasing bandwidth between CPUs and accelerators. It consolidates multiple interfaces into one PCIe connection to the host processor
  • InfiniBand: a high-speed, low-latency interconnect technology that is often used in high-performance computing (HPC) setups. It enables high-speed interconnection between clusters of servers and storage devices
  • Ethernet: this widespread, cost-effective, and flexible networking technology is used to transfer large amounts of data between servers in a data center, which is crucial for many accelerated computing tasks. However, it does not provide the same level of performance as NVLink or InfiniBand
GPUs connected to a CPU using NVLink and PCIe

The following is a diagram illustrating the architecture of GPUs interfaced with a CPU using NVLink and PCIe connectivity methods.

GPUs connected to a CPU using NVLink and PCIe Diagram
Source: NVIDIA.

Accelerated Computing Instances in the Cloud

In the context of cloud computing, accelerated computing instances refer to virtual machine (VM) instances that utilize hardware accelerators, or co-processors, to perform tasks such as floating-point number calculations, graphics processing, and data pattern matching more efficiently than what is achievable with only CPU-run software.

These accelerated computing instances come equipped with various types of hardware accelerators like graphics processing units (GPUs), application-specific integrated circuits (ASICs), or field-programmable gate arrays (FPGAs), which are housed in cloud data centers.

Cloud service providers (CSPs) such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure offer a variety of accelerated computing instances tailored to handle different types of workloads. The primary advantage of using these cloud-based accelerated computing instances is that end users can access powerful, hardware-accelerated computing capabilities on-demand, operating on a pay-as-you-go basis, eliminating the need for large upfront capital investments in hardware.

Amazon Web Services (AWS)

Amazon Web Services (AWS), through Amazon Elastic Compute Cloud (EC2), provides GPU instances, such as the P3, P4, G3, G4, and G5 instance types. These instances utilize NVIDIA GPUs and are well-suited for machine learning (e.g., training new models), high-performance computing (HPC), and graphics-intensive applications. AWS, via Amazon EC2, also offers F1 instances that use FPGAs to enable the delivery of custom hardware accelerations for workloads like genomics, search/analytics, image and video processing, network security, and big data analytics.

In addition, AWS, through Amazon EC2, offers its own custom ASIC accelerators, known as AWS Inferentia and AWS Trainium, which are designed for machine learning workloads, specifically inference and training, respectively.

  • AWS Inferentia (Inf1): offers the lowest cost of inference in the cloud for running deep learning models, providing up to 70% lower cost per inference than comparable GPU instances
  • AWS Trainium (Trn1): the most cost-efficient high-performance deep learning training instance in the cloud, offering up to 50% cost-to-train savings over comparable GPU instances

While AWS’ custom ASIC accelerators cannot replace the advanced functionality of cutting-edge NVIDIA GPUs, they have the potential to deliver performance levels comparable to certain models of NVIDIA GPUs, but at a reduced cost.

Google Cloud

Google’s Cloud TPU instances provide access to tensor processing units (TPUs) – a particular type of application-specific integrated circuit (ASIC) – for machine learning workloads. The company’s latest custom-developed tensor processing unit is known as TPU v4.

Google Cloud’s TPU v4 delivers exascale machine learning (ML) performance, boasting a 10 times increase in ML system performance over its predecessor, the TPU v3. This development significantly enhances energy efficiency, approximately 2-3 times more compared to contemporary ML domain-specific accelerators (DSAs), while reducing carbon dioxide equivalent (CO2e) emissions by roughly 20 times.

Google runs over 90% of its internal AI training on its custom TPUs, with the remaining 10% run on merchant silicon such as NVIDIA GPUs. As transformer workloads increase, they account for a larger proportion of Google’s training mix. For instance, LLMs running on TPUs already constitute over 30% of Google’s training needs. Notably, 100% of Google’s inference workloads are run on TPUs.

In a separate offering, Google Cloud provides Accelerator-Optimized (A2) instances that are equipped with NVIDIA A100 GPUs. These instances are designed for high-performance computing (HPC), data analytics, and machine learning workloads.

Microsoft Azure

Microsoft Azure provides GPU-optimized virtual machine (VM) sizes for compute-intensive, graphics-intensive, and visualization workloads, featuring various GPUs and CPUs from NVIDIA and AMD. The company’s offerings include NCv3-series and NC T4_v3-series for compute-intensive applications, ND A100 v4-series for deep learning and accelerated HPC applications, NV-series and NVv3-series for remote visualization, and NVv4-series for VDI and remote visualization with partitioned GPUs.

Accelerated Computing and Generative AI

Accelerated computing is a critical enabler for the development and implementation of advanced generative artificial intelligence (AI) models. Generative AI involves using algorithms to create new data that is similar in statistical characteristics to the training set, with notable examples in the domains of images, text, and voice.

In the field of generative AI, models like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformers – which include large language models (LLMs) from OpenAI’s ChatGPT – are used. These involve complex mathematical operations, training on large datasets, and require a high amount of computational power and memory. More specifically, model size, complexity per layer, sequence length, and diversification are factors driving up computational needs.

Accelerated computing plays a particularly important role in addressing generative AI’s computational power and memory requirements. Its capabilities can speed up training times, handle large datasets, enable complex models, facilitate real-time generations, and ensure efficient gradient calculations.

1. Speed Up Training Times

The most significant role that accelerated computing plays in generative AI is in reducing the time it takes to train GANs, VAEs, and Transformer models. These models often take days, weeks, or even months to train on traditional CPU-based architectures.

Accelerated computing platforms, such as graphics processing units (GPUs) and tensor processing units (TPUs), are designed for parallel processing. This makes them capable of handling multiple computations simultaneously, drastically reducing training times.

2. Handle Large Datasets

Generative AI models are typically trained on massive datasets. Accelerated computing hardware can process these larger datasets much more efficiently than traditional CPUs. Furthermore, the use of advanced memory architectures, like the High Bandwidth Memory found in some GPUs, allows for the efficient handling of these large datasets during the training process.

3. Enable Complex Models

The increase in computational power enabled by accelerated computing allows for the creation of more complex and larger models, leading to better results. For instance, larger transformer models like GPT-4, which have 170 trillion parameters, are only possible due to accelerated computing.

4. Real-Time Generations

For certain applications, AI models need to generate output in real-time (or near real-time). This is particularly important for interactive applications such as AI in video games and real-time translations. Accelerated computing ensures that these operations are performed swiftly, thereby enabling real-time functionality.

5. Efficient Gradient Calculations

Deep learning models learn by using gradient-based optimization techniques, like backpropagation. These are computational methods that iteratively adjust the parameters of a model, in the direction that minimizes the error or loss function. These calculations are matrix-based and thus are highly parallelizable, making them well-suited for accelerated computing solutions.

Accelerated Computing and AI-Driven Data Centers

The aim of accelerated computing platforms is to speed up compute-intensive workloads, including AI, data analytics, graphics, and scientific computing, across various types of data centers. These encompass enterprise, colocation, hyperscale/cloud, edge, and modular facilities. The primary objective is to enhance workload performance while reducing power consumption and cost per query.

The rise of generative AI and large language models (LLMs) among consumers, internet companies, enterprises, and startups has led to a significant moment in AI adoption. This surge has resulted in a considerable increase in inference deployment in data centers and cloud platforms. Currently, most inference workloads run on CPUs and basic network interface cards (NICs) equipped with processing capabilities. However, due to growing focus on performance, energy efficiency, cost-effectiveness, and power constraints, the industry is shifting towards accelerated computing, utilizing specialized hardware such as GPUs and ASICs.

One vision for the future of modern data centers involves a continuously operating “AI factory” that processes and converts data into intelligence. These facilities would employ AI models such as LLMs, recommender systems, and eventually, reasoning models. Furthermore, they will feature an inference fleet to support a range of workloads, including video processing, text generation, image generation, and 3D graphics for virtual worlds and simulations.

READ MORE: How Data Centers are enabling Artificial Intelligence (AI)

Accelerated Computing vs Compute-Optimized

The choice between accelerated computing and compute-optimized computing is relevant when designing and managing data centers or selecting cloud computing resources. Tasks that can be parallelized, such as machine learning, AI algorithms, graphics rendering, and large-scale simulations, benefit more from accelerated computing. In contrast, tasks that require strong single-threaded performance, like serving web content, databases, or running business applications, perform better on compute-optimized systems.

Below, we compare the key differences between accelerated computing and compute-optimized computing:

Accelerated ComputingCompute-Optimized Computing
DefinitionUses specialized hardware (e.g., GPUs)Uses high-performance CPUs
Use CasesGraphic-intensive, machine learning tasksHigh computations, server workloads
SpeedExtremely fast with suitable tasks (using parallel processing)Fast but general-purpose (single-threaded tasks)
CostCan be expensive due to specialized hardwareTypically less expensive than accelerated
HardwareDependent on specific hardware, such as GPUs, TPUs, or FPGAsLess hardware-dependent; primarily uses high-performance, multi-core CPUs
Mary Zhang covers Data Centers for Dgtl Infra, including Equinix (NASDAQ: EQIX), Digital Realty (NYSE: DLR), CyrusOne, CoreSite Realty, QTS Realty, Switch Inc, Iron Mountain (NYSE: IRM), Cyxtera (NASDAQ: CYXT), and many more. Within Data Centers, Mary focuses on the sub-sectors of hyperscale, enterprise / colocation, cloud service providers, and edge computing. Mary has over 5 years of experience in research and writing for Data Centers.


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