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Whenever the concept of high-performance cloth simulation is brought up, GPU acceleration is almost always the first that comes to mind. Leveraging immense parallelization, GPU algorithms have demonstrated significant success recently, whereas CPU methods are somewhat overlooked. Indeed, the need for an efficient CPU simulator is evident and pressing. In many scenarios, high-end GPUs may be unavailable or are already allocated to other tasks, such as rendering and shading. A high-performance CPU alternative can greatly boost the overall system capability and user experience. Inspired by this demand, this paper proposes a CPU algorithm for high-resolution cloth simulation. By partitioning the garment model into multiple (but not massive) sub-meshes or domains, we assign per-domain computations to individual CPU processors. Borrowing the idea of projective dynamics that breaks the computation into global and local steps, our key contribution is a new parallelization paradigm at domains for both global and local steps so that domain-level calculations are sequential and lightweight. The CPU has much fewer processing units than a GPU. Our algorithm mitigates this disadvantage by wisely balancing the scale of the parallelization and convergence. We validate our method in a wide range of simulation problems involving high-resolution garment models. Performance-wise, our method is at least one order faster than existing CPU methods, and it delivers a similar performance compared with the state-of-the-art GPU algorithms in many examples, but without using a GPU.
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Accelerating 2-D Image Convolution Using a Graphics Processing Unit
Image processing is an important technique that is used in many fields, such as self-driving vehicles or facial recognition. One method is called image convolution, which involves many calculations that manipulate the pixels of an image to produce a new image with a desired effect. This is computation intensive and requires a significant amount of time when run on a traditional computer processing unit (CPU). Since image processing is used for real-time applications, such as those mentioned above, it is essential that convolution algorithms run as quickly as possible. A common way to speed up image convolution algorithms is to take advantage of the highly parallel structure of graphical processing units (GPU) to perform concurrent calculations. One problem with GPU applications is that they are often limited by the latency delays associated with transferring data between the CPU and the GPU. Previous works have looked into different ways to address this issue and optimize GPU programs. This research aims to explore different memory implementations and compare them to see which is best at optimizing data transfers.
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- Award ID(s):
- 1659650
- PAR ID:
- 10314076
- Date Published:
- Journal Name:
- 2021 IEEE Western New York Image and Signal Processing Workshop (WNYISPW)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/WNYISPW53194.2021.9661289
