Title | ||
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Thallo – Scheduling for High-Performance Large-Scale Non-Linear Least-Squares Solvers |
Abstract | ||
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AbstractLarge-scale optimization problems at the core of many graphics, vision, and imaging applications are often implemented by hand in tedious and error-prone processes in order to achieve high performance (in particular on GPUs), despite recent developments in libraries and DSLs. At the same time, these hand-crafted solver implementations reveal that the key for high performance is a problem-specific schedule that enables efficient usage of the underlying hardware. In this work, we incorporate this insight into Thallo, a domain-specific language for large-scale non-linear least squares optimization problems. We observe various code reorganizations performed by implementers of high-performance solvers in the literature, and then define a set of basic operations that span these scheduling choices, thereby defining a large scheduling space. Users can either specify code transformations in a scheduling language or use an autoscheduler. Thallo takes as input a compact, shader-like representation of an energy function and a (potentially auto-generated) schedule, translating the combination into high-performance GPU solvers. Since Thallo can generate solvers from a large scheduling space, it can handle a large set of large-scale non-linear and non-smooth problems with various degrees of non-locality and compute-to-memory ratios, including diverse applications such as bundle adjustment, face blendshape fitting, and spatially-varying Poisson deconvolution, as seen in Figure 1. Abstracting schedules from the optimization, we outperform state-of-the-art GPU-based optimization DSLs by an average of 16× across all applications introduced in this work, and even some published hand-written GPU solvers by 30%+. |
Year | DOI | Venue |
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2021 | 10.1145/3453986 | ACM Transactions on Graphics |
Keywords | DocType | Volume |
DSL, scheduling, GPU, non-linear leastsquares, optimization, 3D Reconstruction | Journal | 40 |
Issue | ISSN | Citations |
5 | 0730-0301 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Michael Mara | 1 | 36 | 4.52 |
Felix Heide | 2 | 329 | 32.29 |
Michael Zollhöfer | 3 | 0 | 0.34 |
Matthias Nießner | 4 | 1373 | 67.28 |
Pat Hanrahan | 5 | 11081 | 1148.97 |