GPU Based Hardware Acceleration of Iterative Closest Point Algorithm Using DPC++
Bibliographic record
Abstract
Ever since the Integrated Circuit was used in the Apollo Guidance Computer, which played a vital role in Apollo moon missions, computers have evolved into different forms to meet various requirements. Hardware accelerators are one such evolution in the realm of computing. They represent specialized hardware components designed to execute specific tasks more efficiently than traditional general-purpose processors. Combining the powers of both general-purpose processors and hardware accelerators is a bleeding-edge research area called heterogeneous computing. Advancements in 3D vision technology today demand faster computation on an unprecedented scale. Shape registration is one such operation predominantly carried out by an algorithm called iterative closest point (ICP). It is a computationally intensive algorithm with a lot of inherent parallelism, making it a suitable candidate for hardware acceleration through heterogeneous computing. In this article, we present our implementation of ICP in DPC++, a latest heterogeneous computing C++ compiler. A speedup of 24.66X and 35.59X were achieved by our CPU-GPU implementations, bruteforce-ICP and KD-tree ICP, respectively, compared to the CPU implementation on the Stanford bunny model point cloud - 35k resolution. Additionally, our GPU implementation of KD-Tree ICP is 2.6 times faster than the widely used state of the art ICP implementation by the Point Cloud Library.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".