SPIRAL: Code Generation for DSP Transforms
By Markus Püschel, José M. F. Moura, Jeremy R. Johnson, D. Padua, Manuela M. Veloso, Bryan Singer, Jianxin Xiong, F. Franchetti, Aca Gacic, Y. Voronenko and 3 more. Proceedings of the IEEE, 2005
975 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Computational Theory and Mathematics, Signal Processing
View Paper: https://www.semanticscholar.org/paper/0221bee380311d75ffcc11fd66be7d5cf632f636
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
In Computational Theory and Mathematics
- Oleksandr Polozov
- J. Demmel
- Alan Edelman
- Frédo Durand
- Andrew Adams
- Koushik Sen
- Sylvain Paris
- Saman P. Amarasinghe
In Signal Processing
- Oleksandr Polozov
- Frédo Durand
- Koushik Sen
- Saman P. Amarasinghe
- Dhruv Batra
- Steven G. Johnson
- Sumit Gulwani
- Connelly Barnes
Citations in the corpus
- Precimonious: Tuning assistant for floating-point precision (2013, background): “Autotuning of codes to improve performance is a very large area of research, just a few citations being [7, 14, 23, 27, 28].”
- The Design and Implementation of FFTW3 (2005, background): “In particular, SPIRAL [9], [10] is another system focused on optimization of Fourier transforms and related algorithms, but it has distinct differences from FFTW. SPIRAL searches at compile time over a space of mathematically equivalent formulas expressed in a “tensor-product” language, whereas…”
- Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples (2018, methodology): “In this work, we build upon deductive search, first studied for synthesis by Manna & Waldinger (1971), and primarily used for program synthesis from formal logical specifications (Puschel et al., 2005; Chaudhari & Damani, 2015).”
- Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines (2013, background): “Split Compilers Sequoia’s “mappings” and SPIRAL’s loop synthesis algebra echo our separation of the model of scheduling from the description of the algorithm, and its lifting outside our compiler [9, 25].”
- PATUS: A Code Generation and Autotuning Framework for Parallel Iterative Stencil Computations on Modern Microarchitectures (2011, background): “Autotuning has been applied successfully in diverse libraries and frameworks for various types of kernels which occur frequently in scientific computing, including ATLAS [2] and FLAME [3] for dense linear algebra, OSKI [4] for sparse linear algebra, FFTW [5] and SPIRAL [6] for signal processing tran”
- PetaBricks: a language and compiler for algorithmic choice (2009, methodology): “Other autotuning systems include SPARSITY (Im and Yelick 2001) for sparse matrix computations, SPIRAL (Puschel et al. 2005) for digital signal processing, UHFFT (Ali et al. 2007) for FFT on multicore systems, OSKI (Vuduc et al. 2005) for sparse matrix kernels, and autotuning frameworks for…”