What is Accelerated Computing? A Comprehensive Guide to the Future of Processing

2024/12/27

The article covers accelerated computing's key hardware, software, networking technologies, applications, benefits, and energy-efficient future.

Accelerated computing uses GPUs, TPUs, and FPGAs to handle parallel processing tasks faster than CPUs. It speeds up complex model training in data-intensive domains like AI and ML. The need for real-time data processing and energy-efficient solutions is driving its growth across sectors. The AI chipset industry may expand from $7.3 billion in 2022 to above $200 billion by 2029, which shows rising demand for accelerated computing solutions.

Key Hardware Accelerators in Accelerated Computing

Graphics Processing Units (GPUs)

GPUs are used in parallel processing for AI and deep learning tasks. Their architecture comprises numerous smaller cores for simultaneous execution of threads. It is effective for matrix operations, which are fundamental in neural network computations. E.g., NVIDIA's A100 GPU delivers up to 312 teraFLOPS performance to accelerate training times for large models. Moreover, GPUs support mixed-precision calculations for speed and accuracy. Yet, they may consume high power and not be the most energy-efficient option for all uses.

Field-Programmable Gate Arrays (FPGAs)

FPGAs offer reconfigurability for customization for specific tasks. It optimizes particular workloads for low latency and improved power efficiency. For example, Microsoft's Project Catapult integrates FPGAs into data centers for better search engine performance and lower power consumption. FPGAs can be reprogrammed to evolve algorithms for flexibility in dynamic environments. Remember, developing FPGA-based solutions demands specialized knowledge. Furthermore, attaining optimal performance might be challenging.

Application-Specific Integrated Circuits (ASICs)

ASICs are developed for high-performance jobs. ASICs accomplish greater throughput and energy savings while customizing the hardware to algorithms. Google's Tensor Processing Unit (TPU) exemplifies this. It delivers 30-80 times higher performance per watt than modern GPUs. ASICs benefit large-scale deployments. That's where consistent workloads justify the development cost. However, they may lack flexibility. Any change in the algorithm needs redesigning the hardware, which prompts longer development cycles and higher expenses.

Software and Hardware Synergy: Example of CUDA

NVIDIA's parallel computing platform CUDA accelerates cross-domain computations using GPU parallelism. Developers may create NVIDIA GPU-compatible programs in C, C++, Fortran, and Python. It facilitates software-hardware synergy for algorithms to run concurrently on many GPU cores. For instance, CUDA's cuDNN library provides optimized routines for deep neural networks to improve training and inference speeds in AI uses. 

Similarly, cuBLAS offers GPU-accelerated implementations of basic linear algebra subprograms for high-performance computing tasks. With specialized libraries, CUDA helps developers utilize GPU capabilities for performance gains in data science and ML.

Interconnect Technologies Enabling Accelerated Computing

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PCI Express (PCIe)

PCIe connects CPUs, GPUs, and other devices at high speeds. Its scalable architecture supports multiple lanes. Each provides up to 16 GT/s in PCIe 4.0 for data transfer between components. In AI and data center environments, PCIe is key to intra-system communication for rapid data exchange between processors and accelerators. PCIe's boundaries necessitate the use of NVLink and CXL for AI applications' better bandwidth and lower latency.

NVLink

GPUs can connect via NVIDIA's NVLink with high bandwidth and low latency. It can surpass the capabilities of PCIe. For instance, For multi-GPU data throughput, NVLink 4.0 delivers a bidirectional bandwidth of up to 900 GB/s per GPU. It benefits deep learning tasks. That's where large datasets are distributed across GPUs. NVLink avoids CPU involvement for lesser latency and greater system performance via GPU-to-GPU communication. For example, NVIDIA's DGX systems use NVLink to link GPUs for AI training.

Compute Express Link (CXL)

CPUs, GPUs, and accelerators may share coherent memory using CXL, an open standard. With the PCIe physical layer, CXL introduces CXL.cache and CXL.mem for low-latency memory access across heterogeneous computing environments. CXL allows dynamic memory pooling and sharing in data centers to adjust resource utilization and memory bottlenecks. It suits AI workloads that need rapid access to large memory spaces for performance and scalability. E.g., the AMD Instinct MI300 series integrates CPU and GPU resources for a unified memory architecture for AI and HPC workloads using CXL.

Ethernet

Ethernet links data center servers, storage, and other devices. Its adoption is thanks to its scalability, cost-effectiveness, and evolution in speed and functionality. Ethernet standards, including 800 GbE, offer enough bandwidth for AI workloads. It coexists with PCIe, NVLink, InfiniBand, and CXL for data transfer between systems. Meanwhile, these specialized interconnects handle intra-system communication. Such a layered approach guarantees efficient data flow within and between servers for AI and HPC applications.

InfiniBand

High-performance networking technology InfiniBand provides low-latency, high-throughput communication. It provides CPU-free Remote Direct Memory Access (RDMA) for low latency in AI clusters and HPC settings. InfiniBand's architecture decreases packet loss and gives reliable data transfer for time-sensitive AI training tasks. Ethernet advances in speed. Yet, InfiniBand maintains latency performance when minimal communication delay is key.

Top Applications of Accelerated Computing

While using GPUs and TPUs, accelerated computing boosts AI/ML model training and inference with parallel processing of large datasets to lower training times from weeks to days. For instance, NVIDIA's H200 Tensor Core GPU employs HBM3e memory, which helps accelerate generative AI and LLMs while handling extensive computations. 

In edge computing and IoT, accelerated computing backs real-time data processing at the network edge to decrease latency and bandwidth usage. Autonomous vehicles and smart cities need immediate data analysis. Besides, in blockchain technology, accelerated computing manages complex cryptographic calculations for transaction processing speeds and network scalability. For example, GPUs expedite the hashing algorithms for blockchain mining for greater throughput and energy efficiency.

Benefits of Accelerated Computing

  • Processing Speed: Increased speed for data-heavy applications.
  • Handling Large Datasets: Improved efficiency for large data processing.
  • Real-Time Data Generation: Enables real-time applications with the least latency.
  • Efficient Gradient Calculations: Ideal for machine learning model training.
  • Enhanced Parallel Processing: Supports simultaneous computation tasks.
  • Energy Efficiency: Decreases power consumption in data centers.
  • Scalability: Easily scaled for growing computational demands.
  • Cost Efficiency: Lowers operational costs through optimized resource usage.
  • Improved Data Throughput: Maximizes data processing per unit time.
  • High-Performance Computing (HPC) Support: Suits the latest scientific simulations.
  • Better AI and Deep Learning Capabilities: Accelerates complex neural networks.
  • Lower Time-to-Insight: Speeds up data analysis for faster decision-making.
  • Greater Resource Utilization: Optimizes hardware use for efficiency.
  • Cutting-Edge Visualization: Supports high-resolution and real-time visual data output.
  • Reliable Fault Tolerance: Lowers disruptions in critical applications.

Accelerated Computing's Role in an Energy-Efficient Future

Accelerated computing with GPU usage saves energy utilization in large data centers. GPUs can be 20 times more energy-efficient than CPUs for AI inference. If all CPU-only servers switched to GPUs, global energy consumption would drop by 10 trillion watt-hours to power 1.4 million homes. The effect is considerably greater in high-performance computing, where GPUs power the top six Green500 systems and are five times more energy-efficient. 

The top 50 Green500 supercomputers (80%) use domain-specific designs to improve power consumption and computational intensity. Data centers may meet global sustainability targets with GPUs for parallel computing to raise throughput and minimize energy waste.