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Details of Grant 

EPSRC Reference: EP/S020861/1
Title: HAMLET: Hardware Enabled Meta-Tracing (ext.)
Principal Investigator: Tratt, Dr L
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Intel Corporation Ltd Mozilla Foundation
Department: Informatics
Organisation: Kings College London
Scheme: EPSRC Fellowship
Starts: 20 July 2019 Ends: 19 July 2022 Value (£): 922,997
EPSRC Research Topic Classifications:
Fundamentals of Computing Software Engineering
EPSRC Industrial Sector Classifications:
Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
17 Jan 2019 EPSRC ICT Prioritisation Panel January 2019 Announced
28 Feb 2019 ICT and DE Fellowship Interviews 28 February 2019 Announced
Summary on Grant Application Form
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.
Key Findings
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Potential use in non-academic contexts
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Impacts
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Summary
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