Shared-memory multi-core processors are ubiquitous, but programming them remains challenging. The programming model exposed by such multi-core processors depends crucially on a "memory consistency model" (MCM), a contract between the hardware and the programmer, which essentially specifies what value a read can return. On the hardware side, one key mechanism to implement the memory consistency model is the "cache-coherence protocol" (CCP), which essentially communicates memory operations between processors. However, the connection between the CCP and the MCM remains unclear. This is especially true for modern CCPs and MCMs, in which CCP design has been divorced from the requirements of the MCM. We argue that this has negatively impacted the scalability and the verifiability of CCPs. On the scalability front, there are serious question marks about sustaining cache coherence as the number of cores continue to scale. On the verification front, the application of existing verification techniques, which do not verify the CCP against the MCM, are arguably broken. In the C3 proposal, we propose a family of CCPs that are "aware" of, and verified against the MCM. Our approach is motivated by the fact that both hardware and programming languages are converging to various relaxed MCMs for performance oriented reasons. We use such relaxed MCMs as inspiration to research CCPs that can take advantage of them. Specifically, we will research "lazy" CCPs where memory operations are batched, and the cost of communicating a memory operation can be amortised. We will also, for the first time, formally verify the relationship between the hardware CCPs and the programmer-oriented MCM they provide. We will investigate rigorously the gains to be had from such lazy CCPs. We will do this by creating a multi-core silicon prototype of our proposed CCP, leveraging our experience in the design of industrial-strength micro-architectures and their implementations.

Technological advances in Micro-Electro-Mechanical Systems (MEMS) are envisaged to allow the dense deployment of nodes with sensing, communication and processing capabilities in large areas for monitoring purposes. In this project we offer an alternative to plain multi hop data forwarding through the sensor network. Our approach suggests the forwarding of sensor data and its storage in selected nodes (storage nodes) from where the data will be collected later on by roaming mobile nodes. This new operational setting will leverage recent advances in mobile technology to relieve the sensor network from heavy multi-hop communication tasks. It will exploit the vast availability of a variety of different mobile devices (e.g., phones, pdas and domain specific wireless-equipped devices such as health monitors) and the potential for user or unmanned vehicle mobility. Mobile devices are equipped with one or more wireless network interfaces (Bluetooth, 802.11 etc), which makes them able to connect and interact with storage nodes in radio range, in an ad hoc manner. An application that would particularly benefit from continous monitoring using sensor nodes is wildlife monitoring. Zoologists would be able to detect social behavior patterns of wild animals (e.g. animal movement patterns), in combination with microclimate conditions, to protect the animals' habitat and ensure their well-being. Current approaches to wildlife monitoring and conservation often still rely on labour intensive techniques for making observations of animal behaviour or for tracking animal movements with established (but outmoded) VHF telemetry equipment. The typical mode of monitoring is to send staff to every single sensor node in the field, to collect sensor readings. The raw data is collected by staff, brought together in a lab, and processed in a centralized manner. The heavy reliance on field-staff for animal monitoring currently incurs considerable employment costs and overheads for ancillary equipment. The use of personnel working alone at night in forests also has significant health and safety implications, and the scrutiny of the Health and Safety Executive is likely to jeopardise many of these protocols in the future.

Technological advances in Micro-Electro-Mechanical Systems (MEMS) are envisaged to allow the dense deployment of nodes with sensing, communication and processing capabilities in large areas for monitoring purposes. In this project we offer an alternative to plain multi-hop data forwarding through the sensor network. Our approach suggests the forwarding of sensor data and its storage in selected nodes (storage nodes) from where the data will be collected later on by roaming mobile nodes. This new operational setting will leverage recent advances in mobile technology to relieve the sensor network from heavy multi-hop communication tasks. It will exploit the vast availability of a variety of different mobile devices (e.g., phones, pdas and domain specific wireless-equipped devices such as health monitors) and the potential for user or unmanned vehicle mobility. Mobile devices are equipped with one or more wireless network interfaces (Bluetooth, 802.11 etc), which makes them able to connect and interact with storage nodes in radio range, in an ad hoc manner. An application that would particularly benefit from continous monitoring using sensor nodes is wildlife monitoring. One of the primary benefits of this new technology will be to offer biologists the means to monitor animals more effectively. The animals too will benefit through the refinements to welfare that these small and efficient RFID devices provide. This entire technology will permit a whole suite of new and more detailed questions about animal movements and spatial behaviour to be answered.Current approaches to wildlife monitoring and conservation often still rely on labour intensive techniques for making observations of animal behaviour or for tracking animal movements with established (but outmoded) VHF telemetry equipment. The typical mode of monitoring is to send staff to every single sensor node in the field, to collect sensor readings. The raw data is collected by staff, brought together in a lab, and processed in a centralized manner. The heavy reliance on field-staff for animal monitoring currently incurs considerable employment costs and overheads for ancillary equipment. The use of personnel working alone at night in forests also has significant health and safety implications, and the scrutiny of the Health and Safety Executive is likely to jeopardise many of these protocols in the future.

As our software systems grow in size and complexity, increasingly diverse users have different wants and needs from their languages: the right language for a statistician (e.g. R) is different from that of someone who formally verifies safety properties (e.g. OCaml), which is different again from someone creating user-facing apps (e.g. Javascript). However, different languages inhabit different silos and interactions between them are crude and slow. Language composition has long been touted as the solution to this problem, allowing languages to be used together in a fine-grained way, but has traditionally struggled to match this promise. In the Lecture Fellowship, my team and I showed that large, messy, real-world languages can be composed together, even allowing different languages to be intermingled within a single line of code. We were able to make the performance of such multi-lingual programs close to their mono-language constituents, showing that language composition's promise is real. However, in the course of this research, an unexpected problem became apparent: Virtual Machines (VMs), the systems used to make many languages run fast (and which are crucial to the good performance of language composition), do not perform as expected. In the largest VM experiment to date, we showed that VMs perform incorrectly in around 60% of cases. Attempts to fix existing VMs have largely failed, because the problems are so deeply embedded that they cannot be teased out, even after careful examination. This is a significant problem for language composition, for which VMs are a foundational pillar. This Fellowship Extension thus aims to show that VMs can have good, predictable performance and that they are a suitable foundational pillar for language composition. However, we cannot expect to create a traditional VM, which often consume tens, hundreds, or thousands of person years of effort. Instead, my team and I will create a new meta-tracing VM system, since history shows that these can be created in a small number of person years. Fortunately for us, meta-tracing has also been shown as the fastest way to run multi-lingual programs, so it is a natural fit. We will rigorously benchmark the new meta-tracing system we create from the beginning of, and throughout, its development. This will enable us to observe performance regressions soon after they occur, allowing us to fix them quickly. We will also take the opportunity to address one of meta-tracing's biggest weaknesses: its slow warmup, that is the time between a program starting, and JIT compilation completing. Tracing currently involves a software interpreter interpreting a software interpreter, with a 100-200x overhead when a loop is traced. We will use the Processor Trace (PT) feature found in recent x86 chips to move the software part of meta-tracing into hardware, giving a roughly 100x speed-up to this critical phase of the system. That will also allow us to be more aggressive in optimising other parts of the tracer that currently cause poor warm-up. At the end of this Fellowship Extension, alongside traditional research papers, we will produce an open-source release of our new meta-tracing system. This will allow others to build on our work, be that for language composition, or simply to make individual languages run fast.