10. Vector, SIMD, GPUs
Vector processing is an efficient technique for handling large datasets by performing operations on multiple data elements simultaneously. This chapter explores SIMD, which enhances parallel computing capabilities in CPUs and GPUs, enabling faster processing for various applications such as graphics rendering and machine learning. Furthermore, advancements in SIMD architectures and the rise of General-Purpose GPUs (GPGPUs) have transformed computation across sectors by efficiently managing vast amounts of parallelizable tasks.
Enroll to start learning
You've not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Vector processing enables parallel execution of the same operation on multiple data points, enhancing performance in tasks like scientific computing and graphics.
- SIMD is a core technology that facilitates the execution of a single instruction across multiple data elements simultaneously.
- GPUs are designed for high-level parallelism and are particularly suited for tasks that involve repetitive operations on large datasets.
Key Concepts
- -- Vector Processing
- A computing technique that applies a single instruction to multiple data elements simultaneously for high performance.
- -- SIMD (Single Instruction, Multiple Data)
- A parallel computing method allowing a single instruction to be executed on multiple data points at once, optimizing data-level parallelism.
- -- GPUs
- Graphics Processing Units specialized for handling large-scale parallel computations, commonly used in graphics, scientific simulations, and machine learning.
- -- Vectorization
- The process of transforming scalar operations into vector operations to enable parallel execution using SIMD instructions.
Additional Learning Materials
Supplementary resources to enhance your learning experience.