Below are some of the research projects actively being
pursued in my group. Besides these, there are a number of other
currently less developed ideas my students and I are seriously exploring, broadly in the
areas of programming language design, program analysis, type systems,
protocol and specification inference, testing, and verification.
Programming Eventually Consistent Data Stores.
Geo-distributed web applications often favor high availability over strong consistency. In response to this bias, modern-day replicated data stores often eschew sequential consistency in favor of weaker eventual consistency data semantics. While most operations supported by a typical web application can be engineered, with sufficient care, to function under EC, there are oftentimes critical operations that require stronger consistency guarantees. Unfortunately, existing approaches to tunable consistency suffers from poorly-defined implementation-specific semantics, often expressed at a level of abstraction far removed from the application. Programming modern-day distributed systems is thus a complex error-prone endeavor.
The Quelea project addresses these concerns by defining a declarative programming model for eventually consistent data stores that abstracts the actual implementation of the data store via high-level programming and system-level models that are agnostic to a specific implementation. By doing so, Quelea frees application programmers from having to reason about their application in terms of low-level implementation specific data store semantics. Instead, programmers can now reason in terms of an abstract model of the data store, and develop applications by defining and composing high-level replicated data types. Quelea is equipped with a formal specification language capable of expressing precise semantics of high-level consistency guarantees (e.g., causal consistency) in the abstract model. Any eventually consistent key-value store can support Quelea by implementing a thin shim layer and a choosen set of high-level consistency guarantees on top of its existing low-level interface.
Machine Learning for Specification and Type Inference.
Having precise specifications that describe a program's intended behavior is a critical pre-requisite to ensure reliabiity, extensibiity, and maintainability of modern-day software. Generating these specifications manually is a challenging, often unsuccessful, exercise; unfortunately, existing static and dynamic analysis techniques often produce poor quality specifications that are ineffective in aiding program verification tasks to guarantee a program behaves as expected.
We have pursued a recent line of work on automated synthesis of specifications that overcome many of the deficiencies that plague existing specification inference methods. Our main contribution is a formulation of the problem as a sample driven one, in which specifications, represented as terms in a decidable refinement type representation, are discovered from observing a program's sample runs in terms of either program execution paths or input-output values, and automatically verified through the use of expressive refinement type systems.
Our approach is realized as a series of inductive synthesis frameworks, which use various logic-based or classification-based learning algorithms to provide sound and precise machine-checked specifications. Experimental results indicate that the learning algorithms are both efficient and effective, capable of automatically producing sophisticated specifications in nontrivial hypothesis domains over a range of complex real-world programs, going well beyond the capabilities of existing solutions.
Verified Compilation and Reasoning for Concurrency.
With colleagues at Cambridge, INRIA, and MPI-SWS, we have built a
compiler for ClightTSO, a C-like language for expressing shared-memory
concurrency above x86 processors. Our approach defines a TSO (total store
order) relaxed-memory semantics for ClightTSO that exposes the processor's
memory model for expressing high-performance (potentially racy) code. We
have implemented a verifying compiler for the language as an extension to
Compcert. We have recently extended this work to consider an alternative
definition of the Java Memory Model that is more closely aligned with the
semantics of hardware memory models such as TSO and Power.
Along with colleagues at Cambridge, and Microsoft Research, we have also
explored formal specification and verification techniques for deterministic
parallelism. We define a high-level specification that expresses the
necessary conditions for correct execution, and examine ways to tie this
high-level specification to low-level implementations. We are particularly
interested in modular reasoning technique that allow us to reason about
program and library correctness without breaking abstraction boundaries. We
have recently applied techniques like frame inference and abduction to
build program analyses that automatically inject synchronization barriers to
enforce determinism in data parallel applications.
Abstractions, compilation, and runtime support for
high-level concurrent programming.
We are working on a number of topics related to the design and
implementation of scalable concurent and parallel functional
programs. Our research centers around extensions to
message-passing dialects of ML (such as CML), software transactions and
transactional events, and associated program analyses. We
are also working on runtime infrastructure to build lightweight and
parallel and concurrent garbage collectors for ML-like languages.
Much of our implementation effort centers around Multi-MLton, a multicore
aware extension of the MLton whole-program optimizing compiler for Standard ML.
In addition, we have an active effort exploring compilation techniques
and runtime support for safe futures, a lightweight annotation for
expressing deterministic parallelism; we 've studied the behavior and
analysis of safe futures for both ML as well as Java.
Program analyses and type systems.
We have developed a compositional and lightweight
dependent type inference system for ML capable of inferring useful
safety properties of ML programs that go beyond the kinds of
properties expressible using classical Hindley-Milner polymorphic
type inference. Our technique infers refinements, requires no
programmer annotations or guidance, is context-sensitive, and
is not based on a whole-program analysis.
We are also looking at a variety of different path-sensitive
static analysis techniques to enable effective automatic mining of
specifications, and to facilitate more effective program
optimizations. Our work on specification inference is in the
context of C and Java, while our current focus on program optimizations
is centered around developing scalable path sensitive analyses for the
SSA intermediate representation in MLton.
for debugging and testing concurrent programs.
Debugging and testing shared memory concurrent
programs is difficult because of complex non-deterministic interactions
introduced by scheduler-induced thread interleavings. We have
explored a number of different dynamic analyses to enable efficent
debugging, replay, and regression testing of large concurrent C and Java
programs. Our techniques involve analyzing core dumps and
execution traces, aligning correct and faulty executions to identify
salient sources of divergence that can be used to identify potential
programming for graph-structured applications.
We investigate linguistic extensions to map/reduce
abstractions for programming large-scale distributed systems, with
special focus on applications that manipulate large, unstructured
graphs. We target real-world graph analysis tasks found in comparative
analysis of biological networks as an important case study.
We address the following specific questions: (i) how can highly
unstructured graph-based formalisms be cast in the map/reduce
framework? (ii) how effectively can these specifications leverage
existing map/reduce infrastructure? (iii) how can these abstractions
and their execution environments be enhanced to provide the semantic
expressiveness necessary for programmability and scalable performance?
(iv) how can these analysis tasks be integrated into comprehensive
scientific resources usable by the wider applications community?
Answers to these questions entail exploration of linguistic extensions
to existing map/reduce abstractions, defining new implementations on
wide-area multicore/SMP platforms, and crafting an expressive graph
analysis toolkit suitable for realistic deployment in important domains
such as systems biology.