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| Version | Summary | Created by | Modification | Content Size | Created at | Operation |
|---|---|---|---|---|---|---|
| 1 | Camila Xu | -- | 1702 | 2022-11-29 01:31:01 |
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A thread block is a programming abstraction that represents a group of threads that can be executed serially or in parallel. For better process and data mapping, threads are grouped into thread blocks. The number of threads varies with available shared memory. 'The number of threads in a thread block is also limited by the architecture to a total of 512 threads per block.' The threads in the same thread block run on the same stream processor. Threads in the same block can communicate with each other via shared memory, barrier synchronization or other synchronization primitives such as atomic operations. Multiple blocks are combined to form a grid. All the blocks in the same grid contain the same number of threads. Since the number of threads in a block is limited to 512, grids can be used for computations that require a large number of thread blocks to operate in parallel. CUDA is a parallel computing platform and programming model that higher level languages can use to exploit parallelism. In CUDA, the kernel is executed with the aid of threads. The thread is an abstract entity that represents the execution of the kernel. A kernel is a small program or a function. Multi threaded applications use many such threads that are running at the same time, to organize parallel computation. Every thread has an index, which is used for calculating memory address locations and also for taking control decisions.
References
- "CUDA Thread Model". https://www.olcf.ornl.gov/kb_articles/cuda-thread-model/.
- "Thread Hierarchy in CUDA Programming". http://cuda-programming.blogspot.com/2012/12/thread-hierarchy-in-cuda-programming.html.
- Kirk, David; Hwu, Wen-mei W (January 28, 2010). Programming Massively Parallel Processors: A Hands-on Approach.
- "Thread Indexing Cheatsheet". https://cs.calvin.edu/courses/cs/374/CUDA/CUDA-Thread-Indexing-Cheatsheet.pdf.
- "Thread Optimizations (University of Mayland)". http://www.umiacs.umd.edu/~ramani/cmsc828e_gpusci/lecture9.pdf.
- Wilt, Nicholas (2013). The CUDA Handbook: A Comprehensive Guide to GPU Programming.
- "Thread Optimizations (University of Mayland)". http://www.umiacs.umd.edu/~ramani/cmsc828e_gpusci/lecture9.pdf.
- "Thread Optimizations (University of Mayland)". http://www.umiacs.umd.edu/~ramani/cmsc828e_gpusci/lecture9.pdf.
- "GPU Computing with CUDA Lecture 2 - CUDA Memories". http://www.bu.edu/pasi/files/2011/07/Lecture2.pdf.
- "Memory Issues in CUDA and Execution Scheduling in CUDA". http://sbel.wisc.edu/Courses/ME964/2012/Lectures/lecture0223.pdf.
- "Effect of Instruction Fetch and Memory Scheduling on GPU Performance". http://www.cc.gatech.edu/fac/hyesoon/gputhread.pdf.
- "CAWS: Criticality-Aware Warp Scheduling for GPGPU Workloads". http://faculty.engineering.asu.edu/carolewu/wp-content/uploads/2012/12/PACT14_CAWS_Lee_final.pdf.
