Linear operators used in iterative methods like conjugate gradient have typically been implemented either as ""matrix-driven"" subroutines backed by explicit sparse or dense matrices, or as ""matrix-free"" subroutines that implement specific linear operations directly (e.g. FFTs). The matrix-driven approach is generally more portable because it can target widely-available BLAS libraries, but it can be inefficient in terms of time and space complexity. In contrast, the matrix-free approach is more performant because it leverages structure in operations, but it requires each operator be re-implemented on each new platform. To increase performance and portability, we propose a hybrid approach that represents linear operators as expression trees. Leaf nodes in the tree are either matrix-free or matrix-driven operators, and interior nodes represent mathematical compositions (sums, products, transposes) or structural compositions (stacks, block diagonals, etc.) of the leaf operators. This representation enables expert-guided reordering and fusion transformations that can improve performance or reduce memory pressure. We implement our approach in a domain-specific language called Indigo. We assess Indigo on image reconstruction problems arising in four application areas: magnetic resonance imaging, ptychography, magnetic particle imaging, and fluorescent microscopy. We give performance results from vendor BLAS libraries, and we introduce specializations to Sparse BLAS routines that achieve near-Roofline performance on multi-core, many-core, and GPU systems.

We propose a new compressive imaging method for reconstructing 2D or 3D objects from their scattered wave-field measurements. Our method relies on a novel, nonlinear measurement model that can account for the multiple scattering phenomenon, which makes the method preferable in applications where linear measurement models are inaccurate. We construct the measurement model by expanding the scattered wave-field with an accelerated-gradient method, which is guaranteed to converge and is suitable for large-scale problems. We provide explicit formulas for computing the gradient of our measurement model with respect to the unknown image, which enables image formation with a sparsity-driven numerical optimization algorithm. We validate the method both analytically and with numerical simulations.

Upon receiving the output sequence streaming from a sequential encoder, a decoder reconstructs the corresponding input sequence that streamed to the encoder. Such an encoding and decoding scheme is commonly encountered in communication, cryptography, signal processing, and other applications. Given an encoder specification, decoder design can be error-prone and time consuming. Its automation may help designers improve productivity and justify encoder correctness. Though recent advances showed promising progress, there is still no complete method that decides whether a decoder exists for a finite state transition system. The quest for completely automatic decoder synthesis remains. This paper presents a complete and practical approach to automating decoder synthesis via incremental Boolean satisfiability solving and Craig interpolation. Experiments show that, for decoder-existent cases, our method synthesizes decoders effectively; for decoder-nonexistent cases, our method concludes the nonexistence instantly while prior methods may fail. Case studies are also conducted in synthesizing decoders for linear error-correcting codes.

Upon receiving the output sequence streaming from a sequential encoder, a decoder reconstructs the corresponding input sequence that streamed to the encoder. Such an encoding and decoding scheme is commonly encountered in communication, cryptography, signal processing, and other applications. Given an encoder specification, decoder design can be error-prone and time consuming. Its automation may help designers improve productivity and justify encoder correctness. Though recent advances showed promising progress, there is still no complete method that decides whether a decoder exists for a finite state transition system. The quest for completely automatic decoder synthesis remains. This paper presents a complete and practical approach to automating decoder synthesis via incremental SAT solving and Craig interpolation. Experiments show that, for decoder-existent cases, our method synthesizes decoders effectively; for decoder-nonexistent cases, our method concludes the non-existence instantly while prior methods may fail.