Computer Science

We present Calyx, a new intermediate language (IL) for compiling high-level programs into hardware designs. Calyx combines a hardware-like structural language with a software-like control flow representation with loops and conditionals. This split representation enables a new class of hardware-focused optimizations that require both structural and control flow information which are crucial for high-level programming models for hardware design. The Calyx compiler lowers control flow constructs using finite-state machines and generates synthesizable hardware descriptions. We have implemented Calyx in an optimizing compiler that translates high-level programs to hardware. We demonstrate Calyx using two DSL-to-RTL compilers, a systolic array generator and one for a recent imperative accelerator language, and compare them to equivalent designs generated using high-level synthesis (HLS). The systolic arrays are 4.6? faster and 1.1? larger on average than HLS implementations, and the HLS-like imperative language compiler is within a few factors of a highly optimized commercial HLS toolchain. We also describe three optimizations implemented in the Calyx compiler.

A theory of data types based on category theory is presented. We organize data types under a new categorical notion of F,G-dialgebras which is an extension of the notion of adjunctions as well as that of T-algebras. T-algebras are also used in domain theory, but while domain theory needs some primitive data types, like products, to start with, we do not need any. Products, coproducts and exponentiations (i.e. function spaces) are defined exactly like in category theory using adjunctions. F,G-dialgebras also enable us to define the natural number object, the object for finite lists and other familiar data types in programming. Furthermore, their symmetry allows us to have the dual of the natural number object and the object for infinite lists (or lazy lists). We also introduce a programming language in a categorical style using F,G-dialgebras as its data type declaration mechanism. We define the meaning of the language operationally and prove that any program terminates using Tait's computability method.

Formal semantics provides rigorous, mathematically precise definitions of programming languages, with which we can argue about program behaviour and program equivalence by formal means; in particular, we can describe and verify our arguments with a proof assistant. There are various approaches to giving formal semantics to programming languages, at different abstraction levels and applying different mathematical machinery: the reason for using the semantics determines which approach to choose. In this paper we investigate some of the approaches that share their roots with traditional relational big-step semantics, such as (a) functional big-step semantics (or, equivalently, a definitional interpreter), (b) pretty-big-step semantics and (c) traditional natural semantics. We compare these approaches with respect to the following criteria: executability of the semantics definition, proof complexity for typical properties (e.g. determinism) and the conciseness of expression equivalence proofs in that approach. We also briefly discuss the complexity of these definitions and the coinductive big-step semantics, which enables reasoning about divergence. To enable the comparison in practice, we present an example language for comparing the semantics: a sequential subset of Core Erlang, a functional programming language, which is used in the intermediate steps of the Erlang/OTP compiler. We have already defined a relational big-step semantics for this language that includes treatment of exceptions and side effects. The aim of this current work is to compare our big-step definition for this language with a variety of other equivalent semantics in different styles from the point of view of testing and verifying code refactorings.

Developing efficient geo-distributed applications is challenging as programmers can easily introduce computations that entail high latency communication. We propose a language design which makes latency explicit and extracts type-level bounds for a computation's runtime latency. We present our initial steps with a core calculus that enables extracting provably correct latency bounds and outline future work.

Protocols provide the unifying glue in concurrent and distributed software today; verifying that message-passing programs conform to such governing protocols is important but difficult. Static approaches based on multiparty session types (MPST) use protocols as types to avoid protocol violations and deadlocks in programs. An elusive problem for MPST is to ensure both protocol conformance and deadlock freedom for implementations with interleaved and delegated protocols. We propose a decentralized analysis of multiparty protocols, specified as global types and implemented as interacting processes in an asynchronous ? -calculus. Our solution rests upon two novel notions: router processes and relative types. While router processes use the global type to enable the composition of participant implementations in arbitrary process networks, relative types extract from the global type the intended interactions and dependencies between pairs of participants. In our analysis, processes are typed using APCP, a type system that ensures protocol conformance and deadlock freedom with respect to binary protocols, developed in prior work. Our decentralized, router-based analysis enables the sound and complete transference of protocol conformance and deadlock freedom from APCP to multiparty protocols.

Lifted (family-based) static analysis by abstract interpretation is capable of analyzing all variants of a program family simultaneously, in a single run without generating any of the variants explicitly. The elements of the underlying lifted analysis domain are tuples, which maintain one property per variant. Still, explicit property enumeration in tuples, one by one for all variants, immediately yields combinatorial explosion. This is particularly apparent in the case of program families that, apart from Boolean features, contain also numerical features with big domains, thus admitting astronomic configuration spaces. The key for an efficient lifted analysis is proper handling of variability-specific constructs of the language (e.g., feature-based runtime tests and #if directives). In this work, we introduce a new symbolic representation of the lifted abstract domain that can efficiently analyze program families with numerical features. This makes sharing between property elements corresponding to different variants explicitly possible. The elements of the new lifted domain are constraint-based decision trees, where decision nodes are labeled with linear constraints defined over numerical features and the leaf nodes belong to an existing single-program analysis domain. To illustrate the potential of this representation, we have implemented an experimental lifted static analyzer, called SPLNUM^2Analyzer, for inferring invariants of C programs. It uses existing numerical domains (e.g., intervals, octagons, polyhedra) from the APRON library as parameters. An empirical evaluation on benchmarks from SV-COMP and BusyBox yields promising preliminary results indicating that our decision trees-based approach is effective and outperforms the tuple-based approach, which is used as a baseline lifted analysis based on abstract interpretation.

While recent progress in quantum hardware open the door for significant speedup in certain key areas, quantum algorithms are still hard to implement right, and the validation of such quantum programs is a challenge. Early attempts either suffer from the lack of automation or parametrized reasoning, or target high-level abstract algorithm description languages far from the current de facto consensus of circuit-building quantum programming languages. As a consequence, no significant quantum algorithm implementation has been currently verified in a scale-invariant manner. We propose Qbricks, the first formal verification environment for circuit-building quantum programs, featuring clear separation between code and proof, parametric specifications and proofs, high degree of proof automation and allowing to encode quantum programs in a natural way, i.e. close to textbook style. Qbricks builds on best practice of formal verification for the classical case and tailor them to the quantum case: we bring a new domain-specific circuit-building language for quantum programs, namely Qbricks-DSL, together with a new logical specification language Qbricks-Spec and a dedicated Hoare-style deductive verification rule named Hybrid Quantum Hoare Logic. Especially, we introduce and intensively build upon HOPS, a higher-order extension of the recent path-sum symbolic representation, used for both specification and automation. To illustrate the opportunity of Qbricks, we implement the first verified parametric implementations of several famous and non-trivial quantum algorithms, including the quantum part of Shor integer factoring (Order Finding - Shor-OF), quantum phase estimation (QPE) - a basic building block of many quantum algorithms, and Grover search. These breakthroughs were amply facilitated by the specification and automated deduction principles introduced within Qbricks.

We introduce a new diagrammatic notation for representing the result of (algebraic) effectful computations. Our notation explicitly separates the effects produced during a computation from the possible values returned, this way simplifying the extension of definitions and results on pure computations to an effectful setting. Additionally, we show a number of algebraic and order-theoretic laws on diagrams, this way laying the foundations for a diagrammatic calculus of algebraic effects. We give a formal foundation for such a calculus in terms of Lawvere theories and generic effects.

Building on the observation that reverse-mode automatic differentiation (AD) -- a generalisation of backpropagation -- can naturally be expressed as pullbacks of differential 1-forms, we design a simple higher-order programming language with a first-class differential operator, and present a reduction strategy which exactly simulates reverse-mode AD. We justify our reduction strategy by interpreting our language in any differential λ -category that satisfies the Hahn-Banach Separation Theorem, and show that the reduction strategy precisely captures reverse-mode AD in a truly higher-order setting.

The polarized SILL programming language uniformly integrates functional programming and session-typed message-passing concurrency. It supports general recursion, asynchronous and synchronous communication, and higher-order programs that communicate channels and processes. We give polarized SILL a domain-theoretic semantics---the first denotational semantics for a language with this combination of features. Session types in polarized SILL denote pairs of domains of unidirectional communications. Processes denote continuous functions between these domains, and process composition is interpreted by a trace operator. We illustrate our semantics by validating expected program equivalences.