We present a formalization of an upcoming standard for floating-point formats for machine learning by the IEEE P3109 working group. This includes a definition of a number of small (< 16 bit) formats and a specification of arithmetic functions that operate on such numbers, as well as format conversion function, including conversions to and from IEEE 754 formats. We report on our experience with the use of an automated theorem prover for verification and analysis of our formalization of the specification, and on the utility of the formalization in future implementations of P3109-compliant hardware and software.

Confidentiality, integrity protection, and high availability, abbreviated to CIA, are essential properties for trustworthy data systems. The rise of cloud computing and the growing demand for multiparty applications however means that building modern CIA systems is more challenging than ever. In response, we present the Confidential Consortium Framework (CCF), a general-purpose foundation for developing secure stateful CIA applications. CCF combines centralized compute with decentralized trust, supporting deployment on untrusted cloud infrastructure and transparent governance by mutually untrusted parties. CCF leverages hardware-based trusted execution environments for remotely verifiable confidentiality and code integrity. This is coupled with state machine replication backed by an auditable immutable ledger for data integrity and high availability. CCF enables each service to bring its own application logic, custom multiparty governance model, and deployment scenario, decoupling the operators of nodes from the consortium that governs them. CCF is open-source and available now at https://github.com/microsoft/CCF.

Computational modelling of metabolic processes has proven to be a useful approach to formulate our knowledge and improve our understanding of core biochemical systems that are crucial to maintaining cellular functions. Towards understanding the broader role of metabolism on cellular decision-making in health and disease conditions, it is important to integrate the study of metabolism with other core regulatory systems and omics within the cell, including gene expression patterns. After quantitatively integrating gene expression profiles with a genome-scale reconstruction of human metabolism, we propose a set of combinatorial methods to reverse engineer gene expression profiles and to find pairs and higher-order combinations of genetic modifications that simultaneously optimize multi-objective cellular goals. This enables us to suggest classes of transcriptomic profiles that are most suitable to achieve given metabolic phenotypes. We demonstrate how our techniques are able to compute beneficial, neutral or "toxic" combinations of gene expression levels. We test our methods on nine tissue-specific cancer models, comparing our outcomes with the corresponding normal cells, identifying genes as targets for potential therapies. Our methods open the way to a broad class of applications that require an understanding of the interplay among genotype, metabolism, and cellular behaviour, at scale.

We present EverCrypt: a comprehensive collection of verified, high-performance cryptographic functionalities available via a carefully designed API. The API provably supports agility (choosing between multiple algorithms for the same functionality) and multiplexing (choosing between multiple implementations of the same algorithm). Through abstraction and zero-cost generic programming, we show how agility can simplify verification without sacrificing performance, and we demonstrate how C and assembly can be composed and verified against shared specifications. We substantiate the effectiveness of these techniques with new verified implementations (including hashes, Curve25519, and AES-GCM) whose performance matches or exceeds the best unverified implementations. We validate the API design with two high-performance verified case studies built atop EverCrypt, resulting in line-rate performance for a secure network protocol and a Merkle-tree library, used in a production blockchain, that supports 2.7 million insertions/sec. Altogether, EverCrypt consists of over 124K verified lines of specs, code, and proofs, and it produces over 29K lines of C and 14K lines of assembly code.

snmalloc is an implementation of malloc aimed at workloads in which objects are typically deallocated by a different thread than the one that had allocated them. We use the term producer/consumer for such workloads. snmalloc uses a novel message passing scheme which returns deallocated objects to the originating allocator in batches without taking any locks. It also uses a novel bump pointer-free list data structure with which just 64-bits of meta-data are sufficient for each 64 KiB slab. On such producer/consumer benchmarks our approach performs better than existing allocators. Snmalloc is available at https://github.com/Microsoft/snmalloc.

We consider the problem of automatically and efficiently computing models of constraints, in the presence of complex background theories such as floating-point arithmetic. Constructing models, or proving that a constraint is unsatisfiable, has various applications, for instance for automatic generation of test inputs. It is well-known that a naïve encoding of constraints into simpler theories (for instance, bit-vectors or propositional logic) often leads to a drastic increase in size, or that it is unsatisfactory in terms of the resulting space and runtime demands. We define a framework for systematic application of approximations in order to improve performance. Our method is more general than previous techniques in the sense that approximations that are neither under- nor over-approximations can be used, and it shows promising performance on practically relevant benchmark problems.

Satisfiability Modulo Theories (SMT) is essential for many practical applications, e.g., in hard- and software verification, and increasingly also in other scientific areas like computational biology. A large number of applications in these areas benefit from bit-precise reasoning over finite-domain variables. Current approaches in this area translate a formula over bit-vectors to an equisatisfiable propositional formula, which is then given to a SAT solver. In this paper, we present a novel stochastic local search (SLS) algorithm to solve SMT problems, especially those in the theory of bit-vectors, directly on the theory level. We explain how several successful techniques used in modern SLS solvers for SAT can be lifted to the SMT level. Experimental results show that our approach can compete with state-of-the-art bit-vector solvers on many practical instances and, sometimes, outperform existing solvers. This offers interesting possibilities in combining our approach with existing techniques, and, moreover, new insights into the importance of exploiting problem structure in SLS solvers for SAT. Our approach is modular and, therefore, extensible to support other theories, potentially allowing SLS to become part of the more general SMT framework.

This paper provides a broad overview of the situation in the area of Parallel Search with a specific focus on Parallel SAT Solving. A set of challenges to researchers is presented which, we believe, must be met to ensure the practical applicability of Parallel SAT Solvers in the future. All these challenges are described informally, but put into perspective with related research results, and a (subjective) grading of difficulty for each of them is provided.

Practical software verification is dominated by two major classes of techniques. The first is model checking, which provides total precision, but suffers from the state space explosion problem. The second is abstract interpretation, which is usually much less demanding, but often returns a high number of false positives. We present Loopfrog, a static analyzer that combines the best of both worlds: the precision of model checking and the performance of abstract interpretation. In contrast to traditional static analyzers, it also provides `leaping' counterexamples to aid in the diagnosis of errors.