Yevgen Voronenko (PhD. thesis, Electrical and Computer Engineering, Carnegie Mellon University, 2008)
Library Generation for Linear Transforms
Comment: ECE/CMU A.G. Milnes outstanding dissertation award 2008
Preprint (2.2 MB)

The development of high-performance numeric libraries has become extraordinarily difficult due to multiple processor cores, vector instruction sets, and deep memory hierarchies. To make things worse, often each library has to be re-implemented and re-optimized, whenever a new platform is released. In this thesis we develop a library generator that completely automates the library development for one important numerical domain: linear transforms, which include the discrete Fourier transform, discrete cosine transforms, filters, and discrete wavelet transforms. The input to our generator is a specification of the transform and a set of recursive algorithms for the transform, represented in a high-level domain-specific language; the output is a C++ library that supports general input size, is vectorized and multithreaded, and provides an optional adaptation mechanism for the memory hierarchy. Further, as we show in extensive benchmarks, the runtime performance of our automatically generated libraries is comparable to and often even higher than the best existing human-written code, including the widely used library FFTW and the commercially developed and maintained Intel Integrated Performance Primitives (IPP) and AMD Performance Library (APL). Our generator automates all library development steps typically performed manually by programmers, such as analyzing the algorithm and finding the set of required recursive functions and base cases, the appropriate restructuring of the algorithm to parallelize for multiple threads and to vectorize for the available vector instruction set and vector length, and performing code level optimizations such as algebraic simplification and others. The key to achieving full automation as well as excellent performance is a proper set of abstraction layers in the form of domain-specific languages called SPL (Signal Processing Language), index-free \sspl, regular \sspl, and intermediate code representation, and the use of rewriting systems to perform all difficult optimizations at a suitable, high level of abstraction. In addition, we demonstrate that our automatic library generation framework enables various forms of customization that would be very costly to perform manually. As examples, we show generated trade-offs between code size and performance, generated Java libraries obtained by modifying the backend, and functional customization for important transform variants.

Learn the current Spiral system, Multithreading, SIMD vectorization, Filtering/Convolution/Wavelet transforms, Discrete/fast Fourier transform, General size libraries