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Program synthesis aims at the automatic generation of programs based on given specifications. Despite significant progress, the inherent complexity of synthesis tasks and the interplay among intention, invention and adaptation limit its scope. A promising yet challenging avenue is the integration of concurrency to enhance synthesis algorithms. While some efforts have applied basic concurrency by parallelizing search spaces, more intricate synthesis scenarios involving interdependent subproblems remain unexplored. In this paper, we focus on string transformation as the target domain and introduce the first concurrent synthesis algorithm that enables asynchronous coordination between deductive and enumerative processes, featuring an asynchronous deducer for dynamic task decomposition, a versatile enumerator for resolving enumeration requests, and an accumulative case splitter for if-then-else condition/branch search and assembling. Our implementation, Synthphonia exhibits substantial performance improvements over state-of-the-art synthesizers, successfully solving 116 challenging string transformation tasks for the first time. 

Fully Homomorphic Encryption (FHE) is a cryptographic technique that enables privacy-preserving computation. State-of-the-art Boolean FHE implementations provide a very low-level interface, usually exposing a limited set of Boolean gates that programmers must use to write their FHE applications. This programming model is unnecessarily restrictive: many Boolean FHE schemes supportprogrammable bootstrapping, an operation that allows evaluation of an arbitrary fixed-size lookup table. However, most modern FHE compilers are only capable of reasoning about traditional Boolean circuits, and therefore struggle to take full advantage of programmable bootstrapping. We present COATL, an FHE compiler that makes use of programmable bootstrapping to produce circuits that are smaller and more efficient than their traditional Boolean counterparts. COATL generates circuits usingarithmetic lookup tables, a novel abstraction we introduce for reasoning about computations in Boolean FHE programs. We demonstrate on a variety of benchmarks that COATL can generate circuits that run up to 1.5× faster than those generated by other state-of-the-art compilation strategies. 

Automated code generation and performance enhancements for sparse tensor algebra have become essential in many real-world applications, such as quantum computing, physical simulations, computational chemistry, and machine learning. General sparse tensor algebra compilers are not always versatile enough to generate asymptotically optimal code for sparse tensor contractions. This paper shows how to generate asymptotically better schedules for complex sparse tensor expressions using kernel fission and fusion. We present generalized loop restructuring transformations to reduce asymptotic time complexity and memory footprint. Furthermore, we present an auto-scheduler that uses a partially ordered set (poset)-based cost model that uses both time and auxiliary memory complexities to prune the search space of schedules. In addition, we highlight the use of Satisfiability Module Theory (SMT) solvers in sparse auto-schedulers to approximate the Pareto frontier of better schedules to the smallest number of possible schedules, with user-defined constraints available at compile-time. Finally, we show that our auto-scheduler can select better-performing schedules and generate code for them. Our results show that the auto-scheduler provided schedules achieve orders-of-magnitude speedup compared to the code generated by the Tensor Algebra Compiler (TACO) for several computations on different real-world tensors.