ASC: Automatically Scalable Computation

We present an architecture designed to transparently and automatically scale the performance of sequential programs as a function of the hardware resources available. The architecture is predicated on a model of computation that views program execution as a walk through the enormous state space composed of the memory and registers of a single-threaded processor. Each instruction execution in this model moves the system from its current point in state space to a deterministic subsequent point. We can parallelize such execution by predictively partitioning the complete path and speculatively executing each partition in parallel. Accurately partitioning the path is a challenging prediction problem. We have implemented our system using a functional simulator that emulates the x86 instruction set, including a collection of state predictors and a mechanism for speculatively executing threads that explore potential states along the execution path. While the overhead of our simulation makes it impractical to measure speedup relative to native x86 execution, experiments on three benchmarks show scalability of up to a factor of 256 on a 1024 core machine when executing unmodified sequential programs.Engineering and Applied Science

DEMO: integrating MPC in big data workflows

Secure multi-party computation (MPC) allows multiple parties to perform a joint computation without disclosing their private inputs. Many real-world joint computation use cases, however, involve data analyses on very large data sets, and are implemented by software engineers who lack MPC knowledge. Moreover, the collaborating parties -- e.g., several companies -- often deploy different data analytics stacks internally. These restrictions hamper the real-world usability of MPC. To address these challenges, we combine existing MPC frameworks with data-parallel analytics frameworks by extending the Musketeer big data workflow manager [4]. Musketeer automatically generates code for both the sensitive parts of a workflow, which are executed in MPC, and the remainder of the computation, which runs on scalable, widely-deployed analytics systems. In a prototype use case, we compute the Herfindahl-Hirschman Index (HHI), an index of market concentration used in antitrust regulation, on an aggregate 156GB of taxi trip data over five transportation companies. Our implementation computes the HHI in about 20 minutes using a combination of Hadoop and VIFF [1], while even "mixed mode" MPC with VIFF alone would have taken many hours. Finally, we discuss future research questions that we seek to address using our approach

Quantum Computation by Communication

We present a new approach to scalable quantum computing--a ``qubus computer''--which realises qubit measurement and quantum gates through interacting qubits with a quantum communication bus mode. The qubits could be ``static'' matter qubits or ``flying'' optical qubits, but the scheme we focus on here is particularly suited to matter qubits. There is no requirement for direct interaction between the qubits. Universal two-qubit quantum gates may be effected by schemes which involve measurement of the bus mode, or by schemes where the bus disentangles automatically and no measurement is needed. In effect, the approach integrates together qubit degrees of freedom for computation with quantum continuous variables for communication and interaction.Comment: final published versio

Towards Autopoietic Computing

A key challenge in modern computing is to develop systems that address complex, dynamic problems in a scalable and efficient way, because the increasing complexity of software makes designing and maintaining efficient and flexible systems increasingly difficult. Biological systems are thought to possess robust, scalable processing paradigms that can automatically manage complex, dynamic problem spaces, possessing several properties that may be useful in computer systems. The biological properties of self-organisation, self-replication, self-management, and scalability are addressed in an interesting way by autopoiesis, a descriptive theory of the cell founded on the concept of a system's circular organisation to define its boundary with its environment. In this paper, therefore, we review the main concepts of autopoiesis and then discuss how they could be related to fundamental concepts and theories of computation. The paper is conceptual in nature and the emphasis is on the review of other people's work in this area as part of a longer-term strategy to develop a formal theory of autopoietic computing.Comment: 10 Pages, 3 figure

Programming Quantum Computers Using Design Automation

Recent developments in quantum hardware indicate that systems featuring more than 50 physical qubits are within reach. At this scale, classical simulation will no longer be feasible and there is a possibility that such quantum devices may outperform even classical supercomputers at certain tasks. With the rapid growth of qubit numbers and coherence times comes the increasingly difficult challenge of quantum program compilation. This entails the translation of a high-level description of a quantum algorithm to hardware-specific low-level operations which can be carried out by the quantum device. Some parts of the calculation may still be performed manually due to the lack of efficient methods. This, in turn, may lead to a design gap, which will prevent the programming of a quantum computer. In this paper, we discuss the challenges in fully-automatic quantum compilation. We motivate directions for future research to tackle these challenges. Yet, with the algorithms and approaches that exist today, we demonstrate how to automatically perform the quantum programming flow from algorithm to a physical quantum computer for a simple algorithmic benchmark, namely the hidden shift problem. We present and use two tool flows which invoke RevKit. One which is based on ProjectQ and which targets the IBM Quantum Experience or a local simulator, and one which is based on Microsoft's quantum programming language Q$\#$.Comment: 10 pages, 10 figures. To appear in: Proceedings of Design, Automation and Test in Europe (DATE 2018