Partial Aborts for Transactions via First Class Continuations

Software transactional memory (STM) has proven to be a useful abstraction for developing concurrent applications where programmers denote transactions with an atomic construct that delimits a collection of reads and writes to shared mutable references. The runtime system then guarantees that all transactions are observed to execute atomically with respect to each other. Traditionally, when the runtime system detects that one transaction conflicts with another, it aborts one of the transactions and restarts its execution from the beginning. This can lead to problems with both execution time and throughput. This thesis presents a novel approach that uses first-class continuations to restart a conflicting transaction at the point of a conflict, avoiding the re-execution of any work from the beginning of the transaction that has not been compromised. In practice, this allows transactions to complete more quickly, decreasing execution time and increasing throughput. The ideas presented in this thesis have been implemented in the context of the Manticore project, an ML-family language with support for parallelism and concurrency. Crucially, this work relies on constant-time continuation capturing via a continuation-passing-style (CPS) transformation and heap-allocated continuations. The partial abort scheme has been implemented as a part of three modern STM implementations: TL2, TinySTM, and NOrec. Experimental results show that, while no base STM implementation is universally best, each partial-abort implementation compares favorably to its full-abort counterpart. In addition to an implementation, this thesis presents a formal semantics for partial aborts. A proof of correctness is given by relating the partial-abort semantics to an analogous full-abort semantics via a simulation. All proofs have been formally verified using the Coq Theorem Prover

Practical and effective higher-order optimizations

Inlining is an optimization that replaces a call to a function with that function’s body. This optimization not only reduces the overhead of a function call, but can expose additional optimization oppor-tunities to the compiler, such as removing redundant operations or unused conditional branches. Another optimization, copy propaga-tion, replaces a redundant copy of a still-live variable with the origi-nal. Copy propagation can reduce the total number of live variables, reducing register pressure and memory usage, and possibly elimi-nating redundant memory-to-memory copies. In practice, both of these optimizations are implemented in nearly every modern com-piler. These two optimizations are practical to implement and effec-tive in first-order languages, but in languages with lexically-scoped first-class functions (aka, closures), these optimizations are no

Seraphim, Cherubim, and Virtual Unicorns: Order and Being in Madeleine L’Engle’s Time Quartet

Discusses the symbolism of the various fantastic and supernatural creatures that inhabit L’Engle’s books

Garbage-Free Abstract Interpretation Through Abstract Reference Counting

Abstract garbage collection is the application of garbage collection to an abstract interpreter. Existing work has shown that abstract garbage collection can improve both the interpreter\u27s precision and performance. Current approaches rely on heuristics to decide when to apply abstract garbage collection. Garbage will build up and impact precision and performance when the collection is applied infrequently, while too frequent applications will bring about their own performance overhead. A balance between these tradeoffs is often difficult to strike. We propose a new approach to cope with the buildup of garbage in the results of an abstract interpreter. Our approach is able to eliminate all garbage, therefore obtaining the maximum precision and performance benefits of abstract garbage collection. At the same time, our approach does not require frequent heap traversals, and therefore adds little to the interpreters\u27s running time. The core of our approach uses reference counting to detect and eliminate garbage as soon as it arises. However, reference counting cannot deal with cycles, and we show that cycles are much more common in an abstract interpreter than in its concrete counterpart. To alleviate this problem, our approach detects cycles and employs reference counting at the level of strongly connected components. While this technique in general works for any system that uses reference counting, we argue that it works particularly well for an abstract interpreter. In fact, we show formally that for the continuation store, where most of the cycles occur, the cycle detection technique only requires O(1) amortized operations per continuation push. We present our approach formally, and provide a proof-of-concept implementation in the Scala-AM framework. We empirically show our approach achieves both the optimal precision and significantly better performance compared to existing approaches to abstract garbage collection