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CUDA Architecture
1. CUDAArchitecture
Prof. Shashikant V. Athawale
Assistant Professor | Computer Engineering
Department | AISSMS College of Engineering,
Kennedy Road, Pune , MH, India - 411001
2. Contents
❖ CUDAArchitecture
❖ Applications of CUDA
❖ Introduction to CUDA C-Write and launch CUDA C
kernels
❖ Manage GPU memory
❖ Manage communication and synchronization
❖ Parallel programming in CUDA- C.
8. CUDA C : The Basics
❖ Based on industry-standard C
❖ A handful of language extensions to allow heterogeneous
programs
❖ Straightforward APIs to manage devices, memory, etc.
❖ Terminology:
➢ Host – The CPU and its memory (host memory)
➢ Device – The GPU and its memory (device memory)
Device
11. Data Transfer Directions Keywords
❖ cudaMemcpyHostToHost
❖ cudaMemcpyHostToDevice
❖ cudaMemcpyDeviceToHost
❖ cudaMemcpyDeviceToDevice
12. Parallel Programming in CUDA C
❖ CUDA brings data-parallel computing to the masses.
❖ CUDA is a scalable parallel programming model.
❖ Program runs on any number of processors without
recompiling.
14. CUDA Uses Extensive Multithreading
❖ CUDA threads express fine-grained data parallelism.
➢ Map threads to GPU threads.
➢ Virtualize the processors.
❖ CUDA thread blocks express coarse-grained parallelism.
➢ Blocks hold arrays of GPU threads, define shared
memory boundaries.
➢ Allow scaling between smaller and larger GPUs.
15. CUDA Uses Extensive Multithreading
❖ GPUs execute thousands of lightweight threads.
➢ In graphics, each thread computes one pixel.
➢ One CUDA thread computes one result (or several
results).
➢ Hardware multithreading & zero-overhead
scheduling.