Tag: GPU

New infra age

Posted on by lechuck park

From Claude with some prompting
This image illustrates the surge in data and the advancement of AI technologies, particularly parallel processing techniques that efficiently handle massive amounts of data. As a result, there is a growing need for infrastructure technologies that can support such data processing capabilities. Technologies like big data processing, parallel processing, direct memory access, and GPU computing have evolved to meet this demand. The overall flow depicts the data explosion, the advancement of AI and parallel processing techniques, and the evolution of supporting infrastructure technologies.

CPU,FPGA,ASIC

Posted on by lechuck park

From Claude with some prompting
The CPU is described as a central processing unit for general-purpose computing, handling diverse tasks with high performance but at a low cost/price ratio.

This image provides an overview of different types of processors and their key characteristics. It compares CPUs, ASICs (Application-Specific Integrated Circuits), FPGAs (Field-Programmable Gate Arrays), and GPUs (Graphics Processing Units).

The ASIC is an application-specific integrated circuit designed for specific tasks like cryptography and AI. It has low performance per price but is highly optimized for its intended use cases.

The FPGA is a reconfigurable processor that allows design changes and prototyping. It has medium performance per price and is suitable for data processing sequences.

The GPU is designed for graphic processing and parallel data processing. It excels at high-performance computing for graphics-intensive applications, but has a medium to high cost/price ratio.

The image highlights the key differences in terms of processing capability, specialization, reconfigurability, performance, and cost among these processor types.

GPU works for

Posted on by lechuck park

From ChatGPT with some prompting
The image is a schematic representation of GPU applications across three domains, emphasizing the GPU’s strength in parallel processing:

Image Processing: GPUs are employed to perform parallel updates on image data, which is often in matrix form, according to graphical instructions, enabling rapid rendering and display of images.

Blockchain Processing: For blockchain, GPUs accelerate the calculation of new transaction hashes and the summing of existing block hashes. This is crucial in the race of mining, where the goal is to compute new block hashes as efficiently as possible.

Deep Learning Processing: In deep learning, GPUs are used for their ability to process multidimensional data, like tensors, in parallel. This speeds up the complex computations required for neural network training and inference.

A common thread across these applications is the GPU’s ability to handle multidimensional data structures—matrices and tensors—in parallel, significantly speeding up computations compared to sequential processing. This parallelism is what makes GPUs highly effective for a wide range of computationally intensive tasks.

Processing UNIT

Posted on by lechuck park

From DALL-E With some prompting

Processing Unit

  • CPU (Central Processing Unit): Central / General
    • Cache/Control Unit (CU)/Arithmetic Logic Unit (ALU)/Pipeline
  • GPU (Graphics Processing Unit): Graphic
    • Massive Parallel Architecture
    • Stream Processor & Texture Units and Render Output Units
  • NPU (Neural Processing Unit): Neural (Matrix Computation)
    • Specialized Computation Units
    • High-Speed Data Transfer Paths
    • Parallel Processing Structure
  • DPU (Data Processing Unit): Data
    • Networking Capabilities & Security Features
    • Storage Processing Capabilities
    • Virtualization Support
  • TPU (Tensor Processing Unit): Tensor
    • Tensor Cores
    • Large On-Chip Memory
    • Parallel Data Paths

Additional Information:

  • NPU and TPU are differentiated by their low power, specialized AI purpose.
  • TPU is developed by Google for large AI models in big data centers and features large on-chip memory.

The diagram emphasizes the specialized nature of NPU and TPU for AI tasks, highlighting their low power consumption and specialized computation capabilities, particularly for neural and tensor computations. It also contrasts these with the more general-purpose capabilities of CPUs and the graphic processing orientation of GPUs. DPU is presented as specialized for handling data-centric tasks involving networking, security, and storage in virtualized environments.

Video/Matrix processing

Posted on by lechuck park

From DALL-E with some prompting
The image highlights the system configuration for graphics-intensive tasks like video processing, emphasizing the use of a dedicated PCIe route instead of the CPU’s general bus for data transmission. This enables the GPU to quickly process image and matrix data in parallel. The direct access provided by the PCIe interface offers a data transfer speed range from 250MB to 1GB/s and more, significantly accelerating machine learning (ML) data processing. This setup provides an optimized pathway not only for rapid video processing but also for data-intensive tasks such as ML.

GPU techs

Posted on by lechuck park

From DALL-E with some prompting
The image illustrates various aspects of GPU technology. Firstly, ‘Multi Input’ and ‘Direct Memory Access’ signify that GPUs efficiently receive data from multiple sources and optimize memory access. PCIe NVMe represents the hardware interface for fast data transfer.

Secondly, ‘Multi Computing’ and ‘Parallel Processing’ highlight the core capabilities of GPUs, which can process multiple operations simultaneously. ‘Nano Superconductivity no loss power’ suggests the use of nano-technology and superconductivity for efficient power transmission without energy loss.

Thirdly, the cooling system of the GPU is essential for managing heat and maintaining performance, indicating the importance of cooling technologies in high-performance computing to keep GPU temperatures stable.

Finally, ‘AI output’ shows that all these technologies are ultimately employed for processing data and outputting results for artificial intelligence applications.

This diagram provides an overview of the entire process of GPU technology, from data input through complex calculations and cooling systems, to the output for AI applications.

NVME OF

Posted on by lechuck park

From DALL-E with some prompting
The diagram depicts the direct connection between CPUs and high-performance NVMe SSDs via PCIe and explains how the NVMe over Fabrics (NVMe-oF) technology extends this over a network. NVMe-oF utilizes the TCP/IP protocol to remotely transmit NVMe commands, and NVMe-oF TCP enables stable data transfers over this protocol, meeting the demands for handling large volumes of data in environments like data centers.