GPU-Accelerated RDP Algorithm for Data Segmentation
| Autor: | Pau Cebrian, Juan Carlos Moure |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0303 health sciences
Multi-core processor Recursion Computer science 02 engineering and technology Energy consumption Frame rate 03 medical and health sciences Task (computing) 0202 electrical engineering electronic engineering information engineering Parallelism (grammar) 020201 artificial intelligence & image processing Massively parallel Algorithm 030304 developmental biology Data compression |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030503703 ICCS (1) |
| Popis: | The Ramer-Douglas-Peucker (RDP) algorithm applies a recursive split-and-merge strategy, which can generate fast, compact and precise data compression for time-critical systems. The use of GPU parallelism accelerates the execution of RDP, but the recursive behavior and the dynamic size of the generated sub-tasks, requires adapting the algorithm to use the GPU resources efficiently. While previous research approaches propose the exploitation of task-based parallelism, our research advocates a general fine-grained solution, which avoids the dynamic and recursive execution of kernels. The segmentation of depth images, a typical application used on autonomous driving, reaches speeds of almost 1000 frames per second for typical workloads using our massively parallel proposal on low-consumption, embedded GPUs. The GPU-accelerated solution is at least an order of magnitude faster than the execution of the same program on multiple CPU cores with similar energy consumption. |
| Databáze: | OpenAIRE |
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