Search for: compile

Abstract Circuit quantum electrodynamics enables the combined use of qubits and oscillator modes. Despite a variety of available gate sets, many hybrid qubit-boson (i.e. qubit-oscillator) operations are realizable only through optimal control theory, which is oftentimes intractable and uninterpretable. We introduce an analytic approach with rigorously proven error bounds for realizing specific classes of operations via two matrix product formulas commonly used in Hamiltonian simulation, the Lie–Trotter–Suzuki and Baker–Campbell–Hausdorff product formulas. We show how this technique can be used to realize a number of operations of interest, including polynomials of annihilation and creation operators, namely $\left(a{)}^p\right(a^{†}{)}^q$for integer $p,q$. We show examples of this paradigm including obtaining universal control within a subspace of the entire Fock space of an oscillator, state preparation of a fixed photon number in the cavity, simulation of the Jaynes–Cummings Hamiltonian, and simulation of the Hong-Ou-Mandel effect. This work demonstrates how techniques from Hamiltonian simulation can be applied to better control hybrid qubit-boson devices. 

Using program synthesis to select instructions for and optimize input programs is receiving increasing attention. However, existing synthesis-based compilers are faced by two major challenges that prohibit the deployment of program synthesis in production compilers: exorbitantly long synthesis times spanning several minutes and hours; and scalability issues that prevent synthesis of complex modern compute and data swizzle instructions, which have been found to maximize performance of modern tensor and stencil workloads. This paper proposes MISAAL, a synthesis-based compiler that employs a novel strategy to use formal semantics of hardware instructions to automatically prune a large search space of rewrite rules for modern complex instructions in an offline stage. MISAAL also proposes a novel methodology to make term-rewriting process in the online stage (at compile-time) extremely lightweight so as to enable programs to compile in seconds. Our results show that MISAAL reduces compilation times by up to a geomean of 16x compared to the state-of-the-art synthesis-based compiler, HYDRIDE. MISAAL also delivers competitive runtime performance against the production compiler for image processing and deep learning workloads, Halide, as well as HYDRIDE across x86, Hexagon and ARM. 

In physics and chemistry, quantum systems are typically modeled using energy constraints formulated as Hamiltonians. Investigations into such systems often focus on the evolution of the Hamiltonians under various initial conditions, an approach summarized as Adiabatic Quantum Computing (AQC). Although this perspective may initially seem foreign to functional programmers, we demonstrate that conventional functional programming abstractions—specifically, the Traversable and Monad type classes—naturally capture the essence of AQC. To illustrate this connection, we introduce EnQ, a functional programming library designed to express diverse optimization problems as energy constraint computations (ECC). The library comprises three core components: generating the solution space, associating energy costs with potential solutions, and searching for optimal or near-optimal solutions. Because EnQ is implemented using standard Haskell, it can be executed directly through conventional classical Haskell compilers. More interestingly, we develop and implement a process to compile EnQ programs into circuits executable on quantum hardware. We validate EnQ’s effectiveness through a number of case studies, demonstrating its capacity to express and solve classical optimization problems on quantum hardware, including search problems, type inference, number partitioning, clique finding, and graph coloring. 

Microcontroller-based embedded systems are vulnerable to memory safety errors and must be robust and responsive because they are often used in unmanned and mission-critical scenarios. The Rust programming language offers an appealing compile-time solution for memory safety but leaves stack overflows unresolved and foils zero-latency interrupt handling. We present Hopter, a Rust-based embedded operating system (OS) that provides memory safety, sys- tem robustness, and interrupt responsiveness to embedded systems while requiring minimal application cooperation. Hopter executes Rust code under a novel finite-stack semantics that converts stack overflows into Rust panics, enabling recovery from fatal errors through stack unwinding and restart. Hopter also employs a novel mechanism called soft-locks so that the OS never disables interrupts. We compare Hopter with other well-known embedded OSes using controlled workloads and report our experience using Hopter to develop a flight control system for a miniature drone and a gateway system for Internet of Things (IoT). We demonstrate that Hopter is well-suited for resource-constrained microcontrollers and supports error recovery for real-time workloads. 

We present SERBERUS, the first comprehensive mitigation for hardening constant-time (CT) code against Spectre attacks (involving the PHT, BTB, RSB, STL, and/or PSF speculation primitives) on existing hardware. SERBERUS is based on three insights. First, some hardware control-flow integrity (CFI) protections restrict transient control-flow to the extent that it may be comprehensively considered by software analyses. Second, conformance to the accepted CT code discipline permits two code patterns that are unsafe in the post-Spectre era. Third, once these code patterns are addressed, all Spectre leakage of secrets in CT programs can be attributed to one of four classes of taint primitives—instructions that can transiently assign a secret value to a publicly-typed register. We evaluate SERBERUS on cryptographic primitives in the OPENSSL, LIBSODIUM, and HACL* libraries. SERBERUS introduces 21.3% runtime overhead on average, compared to 24.9% for the next closest state-of-the-art software mitigation, which is less secure. 

Application networks facilitate communication between the microservices of cloud applications. They are built today using service meshes with low-level specifications that make it difficult to express application-specific functionality (e.g., access control based on RPC fields), and they can more than double the RPC latency. We develop AppNet, a framework that makes it easy to build expressive and high-performance application networks. Developers specify rich RPC processing in a high-level language with generalized match-action rules and built-in state management. We compile the specifications to high-performance code after optimizing where (e.g., client, server) and how (e.g., RPC library, proxy) each RPC processing element runs. The optimization uses symbolic abstraction and execution to judge if different runtime configurations of possibly-stateful RPC processing elements are semantically equivalent for arbitrary RPC streams. Our experiments show that AppNet can express common application network function in only 7-28 lines of code. Its optimizations lower RPC processing latency by up to 82%. 

Rowasu'u is a digital archives project that seeks to reunite A'uwẽ-Xavante individuals with researcher produced documentation of their ancestors, families, bodies, culture, and homelands and eventually provide a platform for the collection and preservation of community knowledge. A'uwẽ-Xavante have a long history of receiving academic researchers including anthropologists, geneticists, biomedical researchers, ecologists, and linguists, but they have had limited access to the documentation and other data produced through these encounters. The Rowasu'u project is working with scholars to compile and make accessible records of more than 60 years of decentralized academic research while partnering with A'uwẽ-Xavante communities historically positioned as the most prominent participants. Our larger aspiration is that in addition to supporting A'uwẽ-Xavante efforts to reclaim their history as recorded by scientists, Rowasu'u will advance Indigenous research governance and data sovereignty as human rights applicable to past as well as future research. This chapter discusses our early progress in developing Rowasu'u using Mukurtu CMS, including the challenges and complexities inherent in navigating local politics in the context of generations of marginalization and exclusion. 

Abstract We present the first catalog of fast radio burst (FRB) host galaxies from CHIME/FRB Outriggers, selected uniformly in the radio and the optical by localizing 81 new bursts to 2″ × ∼ 60″ accuracy using CHIME and the k’niʔatn k’l ⌣ stk’masqt Outrigger station, located 66 km from CHIME. Of the 81 localized bursts, we use the probabilistic association of transients to their hosts algorithm to securely identify 21 new FRB host galaxies, and compile spectroscopic redshifts for 19 systems, 15 of which are newly obtained via spectroscopic observations. The most nearby source is FRB 20231229A, at a distance of 90 Mpc. One burst in our sample is from a previously reported repeating source in a galaxy merger (FRB 20190303A). Three new FRB host galaxies (FRBs 20230203A, 20230703A, and 20231206A) are found toward X-ray and optically selected galaxy clusters, potentially doubling the sample of known galaxy cluster FRBs. A search for radio counterparts reveals that FRB 20231128A is associated with a luminous persistent radio source (PRS) candidate with high significance (P_cc ∼ 10⁻²). If its compactness is confirmed, it would be the nearest known compact PRS atz= 0.1079. Our catalog significantly increases the statistics of the Macquart relation at low redshifts (z < 0.2). In the near future, the completed CHIME/FRB Outriggers array will produce hundreds of FRBs localized with very long baseline interferometry (VLBI). This will significantly expand the known sample and pave the way for future telescopes relying on VLBI for FRB localization. 

Abstract MRI acquisition and reconstruction research has transformed into a computation-driven field. As methods become more sophisticated, compute-heavy, and data-hungry, efforts to reproduce them become more difficult. While the computational MRI research community has made great leaps toward reproducible computational science, there are few tailored guidelines or standards for users to follow. In this review article, we develop a cookbook to facilitate reproducible research for MRI acquisition and reconstruction. Like any good cookbook, we list several recipes, each providing a basic standard on how to make computational MRI research reproducible. And like cooking, we show example flavours where reproducibility may fail due to under-specification. We structure the article, so that the cookbook itself serves as an example of reproducible research by providing sequence and reconstruction definitions as well as data to reproduce the experimental results in the figures. We also propose a community-driven effort to compile an evolving list of best practices for making computational MRI research reproducible.