Program Generation Model at Carnegie Mellon University
Explore the revolutionary Program Generation Model developed at Carnegie Mellon University, enabling automated algorithm selection, compilation, and implementation through common abstractions and operator formulas. Discover the vision behind Spiral for conquering high abstraction levels in computing platforms.
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Presentation Transcript
Carnegie Mellon Program Generation with Spiral: Beyond Transforms Franz Franchetti, Daniel Mcfarlin, Fr d ric de Mesmay, Hao Shen, Tomasz W. W odarczyk, Srinivas Chellappa, Marek R. Telgarsky, Peter A. Milder, Yevgen Voronenko, Qian Yu, James C. Hoe, Jos M. F. Moura, Markus P schel Electrical and Computer Engineering Carnegie Mellon University This work was supported by DARPA DESA program, NSF-NGS/ITR, NSF-ACR, Mercury Inc., and Intel
Carnegie Mellon Vision Behind Spiral Current Future Numerical problem Numerical problem human effort algorithm selection C program automated algorithm selection implementation implementation automated compilation compilation Computing platform Computing platform C code a singularity: Compiler has no access to high level information Challenge: conquer the high abstraction level for complete automation
Carnegie Mellon Main Idea: Program Generation Model: common abstraction = spaces of matching formulas abstraction abstraction p defines rewriting search pick algorithm space architecture space Architectural parameter: Vector length, #processors, Kernel: problem size, algorithm choice optimization
Carnegie Mellon Expressing Kernels as Operator Formulas Matrix-Matrix Multiplication Viterbi Decoder 11 10 01 01 10 10 11 00 11 10 00 01 10 01 11 00 010001 010001 convolutional encoder Viterbi decoder = JPEG 2000 (Wavelet, EBCOT) Synthetic Aperture Radar (SAR) JPEG 2000 Compression matched filtering preprocessing interpolation 2D iFFT entropy coding (EBCOT + MQ) DWT quantization
