Andrew Birrell

Bio: Andrew Birrell is an academic researcher from Microsoft. The author has contributed to research in topics: Transactional memory & Software transactional memory. The author has an hindex of 20, co-authored 32 publications receiving 4419 citations.

Dryad: distributed data-parallel programs from sequential building blocks

Abstract: Dryad is a general-purpose distributed execution engine for coarse-grain data-parallel applications. A Dryad application combines computational "vertices" with communication "channels" to form a dataflow graph. Dryad runs the application by executing the vertices of this graph on a set of available computers, communicating as appropriate through flies, TCP pipes, and shared-memory FIFOs.The vertices provided by the application developer are quite simple and are usually written as sequential programs with no thread creation or locking. Concurrency arises from Dryad scheduling vertices to run simultaneously on multiple computers, or on multiple CPU cores within a computer. The application can discover the size and placement of data at run time, and modify the graph as the computation progresses to make efficient use of the available resources.Dryad is designed to scale from powerful multi-core single computers, through small clusters of computers, to data centers with thousands of computers. The Dryad execution engine handles all the difficult problems of creating a large distributed, concurrent application: scheduling the use of computers and their CPUs, recovering from communication or computer failures, and transporting data between vertices.

A design for high-performance flash disks

Abstract: Most commodity flash disks exhibit very poor performance when presented with writes that are not sequentially ordered. We argue that performance can be significantly improved through the addition of sufficient RAM to hold data structures describing a fine-grain mapping between disk logical blocks and physical flash addresses. We present a design that accomplishes this.

A cooperative internet backup scheme

Abstract: We present a novel peer-to-peer backup technique that allows computers connected to the Internet to back up their data cooperatively: Each computer has a set of partner computers, which collectively hold its backup data. In return, it holds a part of each partner's backup data. By adding redundancy and distributing the backup data across many partners, a highly-reliable backup can be obtained in spite of the low reliability of the average Internet machine. Because our scheme requires cooperation, it is potentially vulnerable to several novel attacks involving free riding (e.g., holding a partner's data is costly, which tempts cheating) or disruption. We defend against these attacks using a number of new methods, including the use of periodic random challenges to ensure partners continue to hold data and the use of disk-space wasting to make cheating unprofitable. Results from an initial prototype show that our technique is feasible and very inexpensive: it appears to be one to two orders of magnitude cheaper than existing Internet backup services.

Semantics of transactional memory and automatic mutual exclusion

Abstract: Software Transactional Memory (STM) is an attractive basis for the development of language features for concurrent programming. However, the semantics of these features can be delicate and problematic. In this paper we explore the tradeoffs between semantic simplicity, the viability of efficient implementation strategies, and the flexibilityof language constructs. Specifically, we develop semantics and type systems for the constructs of the Automatic Mutual Exclusion (AME) programming model; our results apply also to other constructs, such as atomic blocks. With this semantics as a point of reference, we study several implementation strategies. We model STM systems that use in-place update, optimistic concurrency, lazy conflict detection, and roll-back. These strategies are correct only under non-trivial assumptions that we identify and analyze. One important source of errors is that some efficient implementations create dangerous 'zombie' computations where a transaction keeps running after experiencing a conflict; the assumptions confine the effects of these computations.

Flash memory management

Abstract: Flash memory is managed utilizing memory management data structures residing in volatile memory of a flash memory device. The memory management data structures are created and updated each time power is supplied to the memory device. During write operations to the flash memory, specific locations in the flash memory are updated to reflect the current status of the flash memory. When power is interrupted, the memory management data structures are recreated upon reapplication of power. The flash memory is scanned and the information obtained from the specific locations in the flash memory is utilized to construct the memory management data structures. No bad block tables are required. Flash memory is managed to provide relatively good random write performance and to accommodate power interruptions. Applications include the use of flash memory for general purpose computing and devices in which power can fail at any time (due to being unplugged for example).

TensorFlow: a system for large-scale machine learning

Abstract: TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. Tensor-Flow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer: whereas in previous "parameter server" designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate the compelling performance that TensorFlow achieves for several real-world applications.

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Abstract: TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed with little or no change on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hundreds of machines and thousands of computational devices such as GPU cards. The system is flexible and can be used to express a wide variety of algorithms, including training and inference algorithms for deep neural network models, and it has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields, including speech recognition, computer vision, robotics, information retrieval, natural language processing, geographic information extraction, and computational drug discovery. This paper describes the TensorFlow interface and an implementation of that interface that we have built at Google. The TensorFlow API and a reference implementation were released as an open-source package under the Apache 2.0 license in November, 2015 and are available at www.tensorflow.org.

Spark: cluster computing with working sets

Abstract: MapReduce and its variants have been highly successful in implementing large-scale data-intensive applications on commodity clusters. However, most of these systems are built around an acyclic data flow model that is not suitable for other popular applications. This paper focuses on one such class of applications: those that reuse a working set of data across multiple parallel operations. This includes many iterative machine learning algorithms, as well as interactive data analysis tools. We propose a new framework called Spark that supports these applications while retaining the scalability and fault tolerance of MapReduce. To achieve these goals, Spark introduces an abstraction called resilient distributed datasets (RDDs). An RDD is a read-only collection of objects partitioned across a set of machines that can be rebuilt if a partition is lost. Spark can outperform Hadoop by 10x in iterative machine learning jobs, and can be used to interactively query a 39 GB dataset with sub-second response time.

Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing

Abstract: We present Resilient Distributed Datasets (RDDs), a distributed memory abstraction that lets programmers perform in-memory computations on large clusters in a fault-tolerant manner. RDDs are motivated by two types of applications that current computing frameworks handle inefficiently: iterative algorithms and interactive data mining tools. In both cases, keeping data in memory can improve performance by an order of magnitude. To achieve fault tolerance efficiently, RDDs provide a restricted form of shared memory, based on coarse-grained transformations rather than fine-grained updates to shared state. However, we show that RDDs are expressive enough to capture a wide class of computations, including recent specialized programming models for iterative jobs, such as Pregel, and new applications that these models do not capture. We have implemented RDDs in a system called Spark, which we evaluate through a variety of user applications and benchmarks.

Pregel: a system for large-scale graph processing

Abstract: Many practical computing problems concern large graphs. Standard examples include the Web graph and various social networks. The scale of these graphs - in some cases billions of vertices, trillions of edges - poses challenges to their efficient processing. In this paper we present a computational model suitable for this task. Programs are expressed as a sequence of iterations, in each of which a vertex can receive messages sent in the previous iteration, send messages to other vertices, and modify its own state and that of its outgoing edges or mutate graph topology. This vertex-centric approach is flexible enough to express a broad set of algorithms. The model has been designed for efficient, scalable and fault-tolerant implementation on clusters of thousands of commodity computers, and its implied synchronicity makes reasoning about programs easier. Distribution-related details are hidden behind an abstract API. The result is a framework for processing large graphs that is expressive and easy to program.