Over the years, traditional tracing garbage collectors have accumulated assumptions that may not hold in new language designs. For instance, we usually assume that run-time objects do not hold addressable sub-parts and have a size of at least one pointer. These fail in systems striving to eliminate pointers and represent data in a dense, serialized form, such as the Gibbon compiler. We propose a new memory management strategy for language runtimes with mostly serialized heaps. It uses a hybrid, generational collector, where regions are bump-allocated into the young generation and objects are bump-allocated within those regions. Minor collections copy data into larger regions in the old generation, compacting it further. The old generation uses region-level reference counting. The resulting system maintains high performance for data traversal programs, while significantly improving performance on other kinds of allocation patterns.

Many applications are designed to perform traversals on tree-like data structures. Fusing and parallelizing these traversals enhance the performance of applications. Fusing multiple traversals improves the locality of the application. The runtime of an application can be significantly reduced by extracting parallelism and utilizing multi-threading. Prior frameworks have tried to fuse and parallelize tree traversals using coarse-grained approaches, leading to missed fine-grained opportunities for improving performance. Other frameworks have successfully supported fine-grained fusion on heterogeneous tree types but fall short regarding parallelization. We introduce a new framework Orchard built on top of Grafter . Orchard ’s novelty lies in allowing the programmer to transform tree traversal applications by automatically applying fine-grained fusion and extracting heterogeneous parallelism. Orchard allows the programmer to write general tree traversal applications in a simple and elegant embedded Domain-Specific Language (eDSL). We show that the combination of fine-grained fusion and heterogeneous parallelism performs better than each alone when the conditions are met.

Achieving parallel performance and scalability involves making compromises between parallel and sequential computation. If not contained, the overheads of parallelism can easily outweigh its benefits, sometimes by orders of magnitude. Today, we expect programmers to implement this compromise by optimizing their code manually. This process is labor intensive, requires deep expertise, and reduces code quality. Recent work on heartbeat scheduling shows a promising approach that manifests the potentially vast amounts of available, latent parallelism, at a regular rate, based on even beats in time. The idea is to amortize the overheads of parallelism over the useful work performed between the beats. Heartbeat scheduling is promising in theory, but the reality is complicated: it has no known practical implementation.

Recent work showed that compiling functional programs to use dense, serialized memory representations for recursive algebraic datatypes can yield significant constant-factor speedups for sequential programs. But serializing data in a maximally dense format consequently serializes the processing of that data, yielding a tension between density and parallelism. This paper shows that a disciplined, practical compromise is possible. We present Parallel Gibbon, a compiler that obtains the benefits of dense data formats and parallelism. We formalize the semantics of the parallel location calculus underpinning this novel implementation strategy, and show that it is type-safe. Parallel Gibbon exceeds the parallel performance of existing compilers for purely functional programs that use recursive algebraic datatypes, including, notably, abstract-syntax-tree traversals as in compilers.

We describe the design and implementation of DetTrace, a reproducible container abstraction for Linux implemented in user space. All computation that occurs inside a DetTrace container is a pure function of the initial filesystem state of the container. Reproducible containers can be used for a variety of purposes, including replication for fault-tolerance, reproducible software builds and reproducible data analytics. We use DetTrace to achieve, in an automatic fashion, reproducibility for 12,130 Debian package builds, containing over 800 million lines of code, as well as bioinformatics and machine learning workflows. We show that, while software in each of these domains is initially irreproducible, DetTrace brings reproducibility without requiring any hardware, OS or application changes. DetTrace's performance is dictated by the frequency of system calls: IO-intensive software builds have an average overhead of 3.49x, while a compute-bound bioinformatics workflow is under 2%.

When writing today's distributed programs, which frequently span both devices and cloud services, programmers are faced with complex decisions and coding tasks around coping with failure, especially when these distributed components are stateful. If their application can be cast as pure data processing, they benefit from the past 40--50 years of work from the database community, which has shown how declarative database systems can completely isolate the developer from the possibility of failure in a performant manner. Unfortunately, while there have been some attempts at bringing similar functionality into the more general distributed programming space, a compelling general-purpose system must handle non-determinism, be performant, support a variety of machine types with varying resiliency goals, and be language agnostic, allowing distributed components written in different languages to communicate. This paper introduces Ambrosia, the first system to satisfy all these requirements. We coin the term "virtual resiliency", analogous to virtual memory, for the platform feature which allows failure oblivious code to run in a failure resilient manner. We also introduce novel programming language constructs for resiliently handling non-determinism. Of further interest is the effective reapplication of much database performance optimization technology to make Ambrosia more performant than many of today's non-resilient cloud solutions.

Applications in many domains are based on a series of traversals of tree structures, and fusing these traversals together to reduce the total number of passes over the tree is a common, important optimization technique. In applications such as compilers and render trees, these trees are heterogeneous: different nodes of the tree have different types. Unfortunately, prior work for fusing traversals falls short in different ways: they do not handle heterogeneity; they require using domain-specific languages to express an application; they rely on the programmer to aver that fusing traversals is safe, without any soundness guarantee; or they can only perform coarse-grain fusion, leading to missed fusion opportunities. This paper addresses these shortcomings to build a framework for fusing traversals of heterogeneous trees that is automatic, sound, and fine-grained. We show across several case studies that our approach is able to allow programmers to write simple, intuitive traversals, and then automatically fuse them to substantially improve performance.

In a typical data-processing program, the representation of data in memory is distinct from its representation in a serialized form on disk. The former has pointers and arbitrary, sparse layout, facilitating easy manipulation by a program, while the latter is packed contiguously, facilitating easy I/O. We propose a language, LoCal, to unify in-memory and serialized formats. LoCal extends a region calculus into a location calculus, employing a type system that tracks the byte-addressed layout of all heap values. We formalize LoCal and prove type safety, and show how LoCal programs can be inferred from unannotated source terms.

Abstract Generalized Algebraic Data Types, or simply GADTs, can encode non-trivial properties in the types of the constructors. Once such properties are encoded in a datatype, however, all code manipulating that datatype must provide proof that it maintains these properties in order to typecheck. In this paper, we take a step toward gradualizing these obligations. We introduce a tool, Ghostbuster, that produces simplified versions of GADTs which elide selected type parameters, thereby weakening the guarantees of the simplified datatype in exchange for reducing the obligations necessary to manipulate it. Like ornaments , these simplified datatypes preserve the recursive structure of the original, but unlike ornaments, we focus on information-preserving bidirectional transformations. Ghostbuster generates type-safe conversion functions between the original and simplified datatypes, which we prove are the identity function when composed. We evaluate a prototype tool for Haskell against thousands of GADTs found on the Hackage package database, generating simpler Haskell'98 datatypes and round-trip conversion functions between the two.

Linear type systems have a long and storied history, but not a clear path forward to integrate with existing languages such as OCaml or Haskell. In this paper, we study a linear type system designed with two crucial properties in mind: backwards-compatibility and code reuse across linear and non-linear users of a library. Only then can the benefits of linear types permeate conventional functional programming. Rather than bifurcate types into linear and non-linear counterparts, we instead attach linearity to function arrows . Linear functions can receive inputs from linearly-bound values, but can also operate over unrestricted, regular values. To demonstrate the efficacy of our linear type system — both how easy it can be integrated in an existing language implementation and how streamlined it makes it to write programs with linear types — we implemented our type system in ghc, the leading Haskell compiler, and demonstrate two kinds of applications of linear types: mutable data with pure interfaces; and enforcing protocols in I/O-performing functions.

We introduce Refinement Reflection , a new framework for building SMT-based deductive verifiers. The key idea is to reflect the code implementing a user-defined function into the function’s (output) refinement type. As a consequence, at uses of the function, the function definition is instantiated in the SMT logic in a precise fashion that permits decidable verification. Reflection allows the user to write equational proofs of programs just by writing other programs using pattern-matching and recursion to perform case-splitting and induction. Thus, via the propositions-as-types principle, we show that reflection permits the specification of arbitrary functional correctness properties. Finally, we introduce a proof-search algorithm called Proof by Logical Evaluation that uses techniques from model checking and abstract interpretation, to completely automate equational reasoning. We have implemented reflection in Liquid Haskell and used it to verify that the widely used instances of the Monoid, Applicative, Functor, and Monad typeclasses actually satisfy key algebraic laws required to make the clients safe, and have used reflection to build the first library that actually verifies assumptions about associativity and ordering that are crucial for safe deterministic parallelism.

When written idiomatically in most programming languages, programs that traverse and construct trees operate over pointer-based data structures, using one heap object per-leaf and per-node. This representation is efficient for random access and shape-changing modifications, but for traversals, such as compiler passes, that process most or all of a tree in bulk, it can be inefficient. In this work we instead compile tree traversals to operate on pointer-free pre-order serializations of trees. On modern architectures such programs often run significantly faster than their pointer-based counterparts, and additionally are directly suited to storage and transmission without requiring marshaling. We present a prototype compiler, Gibbon, that compiles a small first-order, purely functional language sufficient for tree traversals. The compiler transforms this language into intermediate representation with explicit pointers into input and output buffers for packed data. The key compiler technologies include an effect system for capturing traversal behavior, combined with an algorithm to insert destination cursors. We evaluate our compiler on tree transformations over a real-world dataset of source-code syntax trees. For traversals touching the whole tree, such as maps and folds, packed data allows speedups of over 2x compared to a highly-optimized pointer-based baseline.

A key part of implementing high-level languages is providing built- in and default data structures. Yet selecting good defaults is hard. A mutable data structure’s workload is not known in advance, and it may shift over its lifetime—e.g., between read-heavy and write- heavy, or from heavy contention by multiple threads to single- threaded or low-frequency use. One idea is to switch implementa- tions adaptively, but it is nontrivial to switch the implementation of a concurrent data structure at runtime. Performing the transition requires a concurrent snapshot of data structure contents, which normally demands special engineering in the data structure’s de- sign. However, in this paper we identify and formalize an relevant property of lock-free algorithms. Namely, lock-freedom is su cient to guarantee that freezing memory locations in an arbitrary order will result in a valid snapshot. Several functional languages have data structures that freeze and thaw, transitioning between mutable and immutable, such as Haskell vectors and Clojure transients, but these enable only single-threaded writers. We generalize this approach to augment an arbitrary lock-free data structure with the ability to gradually freeze and optionally transition to a new representation. This aug- mentation doesn’t require changing the algorithm or code for the data structure, only replacing its datatype for mutable references with a freezable variant. In this paper, we present an algorithm for lifting plain to adaptive data and prove that the resulting hy- brid data structure is itself lock-free, linearizable, and simulates the original. We also perform an empirical case study in the context of heating up and cooling down concurrent maps.

Existing techniques for injecting probes into running applications are limited;

A core, but often neglected, aspect of a programming language design is its memory (consistency) model. Sequential consistency~(SC) is the most intuitive memory model for programmers as it guarantees sequential composition of instructions and provides a simple abstraction of shared memory as a single global store with atomic read and writes. Unfortunately, SC is widely considered to be impractical due to its associated performance overheads.

Achieving determinism on real software systems remains difficult. Even a batch-processing job, whose task is to map input bits to output bits, risks nondeterminism from thread scheduling, system calls, CPU instructions, and leakage of environmental information such as date or CPU model. In this work, we present a system for achieving low-overhead deterministic execution of batch-processing programs that read and write the file system—turning them into pure functions on files. We allow multi-process executions where a permissions system prevents races on the file system. Process separation enables different processes to enforce permissions and enforce determinism using distinct mechanisms. Our prototype, DetFlow, allows a statically-typed coordinator process to use shared-memory parallelism, as well as invoking process-trees of sandboxed legacy binaries. DetFlow currently implements the coordinator as a Haskell program with a restricted I/O type for its main function: a new monad we call DetIO. Legacy binaries launched by the coordinator run concurrently, but internally each process schedules threads sequentially, allowing dynamic determinism-enforcement with predictably low overhead. We evaluate DetFlow by applying it to bioinformatics data pipelines and software build systems. DetFlow enables determinizing these data-processing workflows by porting a small amount of code to become a statically-typed coordinator. This hybrid approach of static and dynamic determinism enforcement permits freedom where possible but restrictions where necessary.

Generalized Algebraic Dataypes, or simply GADTs, can encode non-trivial properties in the types of the constructors. Once such properties are encoded in a datatype, however, all code manipulating that datatype must provide proof that it maintains these properties in order to typecheck. In this paper, we take a step towards gradualizing these obligations. We introduce a tool, Ghostbuster, that produces simplified versions of GADTs which elide selected type parameters, thereby weakening the guarantees of the simplified datatype in exchange for reducing the obligations necessary to manipulate it. Like ornaments, these simplified datatypes preserve the recursive structure of the original, but unlike ornaments we focus on information-preserving bidirectional transformations. Ghostbuster generates type-safe conversion functions between the original and simplified datatypes, which we prove are the identity function when composed. We evaluate a prototype tool for Haskell against thousands of GADTs found on the Hackage package database, generating simpler Haskell'98 datatypes and round-trip conversion functions between the two.

Dynamic probe injection is now a widely used method to debug performance in production. Current techniques for dynamic probing of native code, however, rely on an expensive stop-the-world approach: binary changes are made within a safe state of the program---typically in which all the program threads are halted---to ensure that another thread executing the modified code region doesn't step into a partially-modified code. Stop-the-world patching is not scalable. In contrast, low overhead, scalable probes that can be rapidly toggled on and off in-place would open up new use cases for statistical profilers and language implementations, even traditional ahead-of-time, native-code compilers. In this paper we introduce safe cross-modification protocols that mutate x86 code between threads but do not require quiescing threads, resulting in radically lower overheads than existing solutions. A key problem is handling instructions that straddle cache lines. We empirically evaluate existing x86 architectures to derive a safe policy given current processor behavior, and we argue that future architectures should clarify the semantics of instruction fetching to make cheap cross-modification easier and future proof.

Given the sophistication of recent type systems, unification-based type-checking and inference can be a time-consuming phase of compilation---especially when union types are combined with subtyping. It is natural to consider improving performance through parallelism, but these algorithms are challenging to parallelize due to complicated control structure and difficulties representing data in a way that is both efficient and supports concurrency. We provide techniques that address these problems based on the LVish approach to deterministic-by-default parallel programming. We extend LVish with Saturating LVars, the first LVars implemented to release memory during the object's lifetime. Our design allows us to achieve a parallel speedup on worst-case (exponential) inputs of Hindley-Milner inference, and on the Typed Racket type-checking algorithm, which yields up an 8.46× parallel speedup on 14 cores for type-checking examples drawn from the Racket repository.

Abstract Graphics Processing Units (GPUs) offer potential for very high performance; they are also rapidly evolving. Obsidian is an embedded language (in Haskell) for implementing high performance kernels to be run on GPUs. We would like to have our cake and eat it too; we want to raise the level of abstraction beyond CUDA code and still give the programmer control over the details relevant to kernel performance. To that end, Obsidian provides array representations that guarantee elimination of intermediate arrays while also using the type system to model the hierarchy of the GPU. Operations are compiled very differently depending on what level of the GPU they target, and as a result, the user is gently constrained to write code that matches the capabilities of the GPU. Thus, we implement not Nested Data Parallelism, but a more limited form that we call Hierarchical Data Parallelism. We walk through case-studies that demonstrate how to use Obsidian for rapid design exploration or auto-tuning, resulting in performance that compares well to the hand-tuned kernels used in Accelerate and NVIDIA Thrust.

Embedded languages are often compiled at application runtime; thus, embedded compile-time errors become application runtime errors. We argue that advanced type system features, such as GADTs and type families, play a crucial role in minimising such runtime errors. Specifically, a rigorous type discipline reduces runtime errors due to bugs in both embedded language applications and the implementation of the embedded language compiler itself. In this paper, we focus on the safety guarantees achieved by type preserving compilation. We discuss the compilation pipeline of Accelerate, a high-performance array language targeting both multicore CPUs and GPUs, where we are able to preserve types from the source language down to a low-level register language in SSA form. Specifically, we demonstrate the practicability of our approach by creating a new type-safe interface to the industrial-strength LLVM compiler infrastructure, which we used to build two new Accelerate backends that show competitive runtimes on a set of benchmarks across both CPUs and GPUs.

Purely functional data structures stored inside a mutable variable provide an excellent concurrent data structure—obviously correct, cheap to create, and supporting snapshots. They are not, however, scalable. We provide a way to retain the benefits of these pure-in-a-box data structures while dynamically converting to a more scalable lock-free data structure under contention. Our solution scales to any pair of pure and lock-free container types with key/value set semantics, while retaining lock-freedom. We demonstrate the principle in action on two very different platforms: first in the Glasgow Haskell Compiler and second in Java. To this end we extend GHC to support lock-free data structures and introduce a new approach for safe CAS in a lazy language.

In distributed applications, the transmission of non-contiguous data structures is greatly slowed down by the need to serialize them into a buffer before sending. We describe Compact Normal Forms, an API that allows programmers to explicitly place immutable heap objects into regions, which can both be accessed like ordinary data as well as efficiently transmitted over the network. The process of placing objects into compact regions (essentially a copy) is faster than any serializer and can be amortized over a series of functional updates to the data structure in question. We implement this scheme in the Glasgow Haskell Compiler and show that even with the space expansion attendant with memory-oriented data structure representations, we achieve between x2 and x4 speedups on fast local networks with sufficiently large data structures.

Graphics processing units (GPUs) can effectively accelerate many applications, but their applicability has been largely limited to problems whose solutions can be expressed neatly in terms of linear algebra. Indeed, most GPU programming languages limit the user to simple data structures - typically only multidimensional rectangular arrays of scalar values. Many algorithms are more naturally expressed using higher level language features, such as algebraic data types (ADTs) and first class procedures, yet building these structures in a manner suitable for a GPU remains a challenge. We present a region-based memory management approach that enables rich data structures in Harlan, a language for data parallel computing. Regions enable rich data structures by providing a uniform representation for pointers on both the CPU and GPU and by providing a means of transferring entire data structures between CPU and GPU memory. We demonstrate Harlan's increased expressiveness on several example programs and show that Harlan performs well on more traditional data-parallel problems.

A fundamental challenge of parallel programming is to ensure that the observable outcome of a program remains deterministic in spite of parallel execution. Language-level enforcement of determinism is possible, but existing deterministic-by-construction parallel programming models tend to lack features that would make them applicable to a broad range of problems. Moreover, they lack extensibility: it is difficult to add or change language features without breaking the determinism guarantee.

Deterministic-by-construction parallel programming models offer the advantages of parallel speedup while avoiding the nondeterministic, hard-to-reproduce bugs that plague fully concurrent code. A principled approach to deterministic-by-construction parallel programming with shared state is offered by LVars: shared memory locations whose semantics are defined in terms of an application-specific lattice. Writes to an LVar take the least upper bound of the old and new values with respect to the lattice, while reads from an LVar can observe only that its contents have crossed a specified threshold in the lattice. Although it guarantees determinism, this interface is quite limited.

Modern parallel computing hardware demands increasingly specialized attention to the details of scheduling and load balancing across heterogeneous execution resources that may include GPU and cloud environments, in addition to traditional CPUs. Many existing solutions address the challenges of particular resources, but do so in isolation, and in general do not compose within larger systems. We propose a general, composable abstraction for execution resources, along with a continuation-based meta-scheduler that harnesses those resources in the context of a deterministic parallel programming library for Haskell. We demonstrate performance benefits of combined CPU/GPU scheduling over either alone, and of combined multithreaded/distributed scheduling over existing distributed programming approaches for Haskell.

We present a new programming model for deterministic parallel computation in a pure functional language. The model is monadic and has explicit granularity, but allows dynamic construction of dataflow networks that are scheduled at runtime, while remaining deterministic and pure. The implementation is based on monadic concurrency, which has until now only been used to simulate concurrency in functional languages, rather than to provide parallelism. We present the API with its semantics, and argue that parallel execution is deterministic. Furthermore, we present a complete work-stealing scheduler implemented as a Haskell library, and we show that it performs at least as well as the existing parallel programming models in Haskell.

This paper presents our experience using a domain-specific functional language, WaveScript, to build embedded sensing applications used in scientific research. We focus on a recent computervision application for detecting birds in their natural environment. The application was ported from a prototype in C++. In reimplementing the application, we gained a much cleaner factoring of its functionality (through higher-order functions and better interfaces to libraries) and a near-linear parallel speed-up with no additional effort. These benefits are offset by one substantial downside: the lack of familiarity with the language of the original vision researchers, who understandably tried to use the language in the familiar way they use C++ and thus ran into various problems.

Sensors capable of sensing phenomena at high data rates on the order of tens to hundreds of thousands of samples per second are now widely deployed in many industrial, civil engineering, scientific, networking, and medical applications. In aggregate, these sensors easily generate several million samples per second that must be processed within milliseconds or seconds. The computation required includes both signal processing and event stream processing. XStream is a stream processing system for such applications. XStream introduces a new data type, the signal segment, which allows applications to manipulate isochronous (regularly spaced in time) collections of sensor samples more conveniently and efficiently than the asynchronous representation used in previous work. XStream includes a memory manager and scheduler optimizations tuned for processing signal segments at high speeds. In benchmark comparisons, we show that XStream outperforms a leading commercial stream processing system by more than three orders of magnitude. On one application, the commercial system processed 72.7 Ksamples/sec, while XStream processed 97.6 Msamples/sec.

Distributed acoustic sensing underlies an increasingly important class of sensor network applications, from habitat monitoring and bioacoustic census to security applications and virtual fences. VoxNet is a complete hardware and software platform for distributed acoustic monitoring applications that focuses on three key goals: (1) rapid deployment in realistic environments; (2) a high level programming language that abstracts the user from platform and network details and compiles into a high performance distributed application; and (3) an interactive usage model based on run-time installable programs, with the ability to run the same high level program seamlessly over live or stored data. The VoxNet hardware is self-contained and weather-resistant, and supports a four-channel microphone array with automated time synchronization, localization, and network coordination. Using VoxNet, an investigator can visualize phenomena in real-time, develop and tune online analysis, and record raw data for off-line analysis and archival. This paper describes both the hardware and software elements of the platform, as well as the architecture required to support distributed programs running over a heterogeneous network. We characterize the performance of the platform, using both microbenchmarks that evaluate specific aspects of the platform and a real application running in the field.

Applications that combine live data streams with embedded, parallel, and distributed processing are becoming more commonplace. WaveScript is a domain-specific language that brings high-level, type-safe, garbage-collected programming to these domains. This is made possible by three primary implementation techniques, each of which leverages characteristics of the streaming domain. First, we employ a novel evaluation strategy that uses a combination of interpretation and reification to partially evaluate programs into stream dataflow graphs. Second, we use profile-driven compilation to enable many optimizations that are normally only available in the synchronous (rather than asynchronous) dataflow domain. Finally, we incorporate an extensible system for rewrite rules to capture algebraic properties in specific domains (such as signal processing).

This paper investigates an application of mobile sensing: detecting and reporting the surface conditions of roads. We describe a system and associated algorithms to monitor this important civil infrastructure using a collection of sensor-equipped vehicles. This system, which we call the Pothole Patrol (P2), uses the inherent mobility of the participating vehicles, opportunistically gathering data from vibration and GPS sensors, and processing the data to assess road surface conditions. We have deployed P2 on 7 taxis running in the Boston area. Using a simple machine-learning approach, we show that we are able to identify potholes and other severe road surface anomalies from accelerometer data. Via careful selection of training data and signal features, we have been able to build a detector that misidentifies good road segments as having potholes less than 0.2% of the time. We evaluate our system on data from thousands of kilometers of taxi drives, and show that it can successfully detect a number of real potholes in and around the Boston area. After clustering to further reduce spurious detections, manual inspection of reported potholes shows that over 90% contain road anomalies in need of repair.

The development of high-level programming environments is essential if wireless sensor networks are to be accessible to non-experts. In this paper, we present the Regiment system, which consists of a high-level language for spatiotemporal macroprogramming, along with a compiler that translates global programs into node-level code. In Regiment, the programmer views the network as a set of spatially-distributed data streams. The programmer can manipulate sets of these streams that may be defined by topological or geographic relationships between nodes. Regiment provides a rich set of primitives for processing data on individual streams, manipulating regions, performing aggregation over a region, and triggering new computation within the network.

A great deal of recent work has investigated new programming abstractions and models for sensor networks. However, the complexity of such systems demands a great deal of effort to develop appropriate compilers and runtime platforms to achieve good performance. We will demonstrate Flask [5], a new programming platform for sensor networks that decouples the design of a high-level programming environment from the the low-level details of generating per-node code and an efficient runtime system.

WaveScope is a data management and continuous sensor data system that integrates relational database and signal processing operations into a single system. WaveScope is motivated by a large number of signal-oriented streaming sensor applications, such as: preventive maintenance of industrial equipment; detection of fractures and ruptures in various structures; in situ animal behavior studies using acoustic sensing; network traffic analysis; and medical applications such as anomaly detection in EKGs. These target applications use a variety of embedded sensors, each sampling at fine resolution and producing data at high rates ranging from hundreds to hundreds of thousands of samples per second. Though there has been some work on applications in the sensor network community that do this kind of signal processing (for example, shooter localization [5], industrial equipment monitoring [4], and urban infrastructure monitoring [2]), these applications are typically custombuilt and do not provide reusable high-level programming framework suitable for easily building new signal processing applications with similar functionality. This poster shows how WaveScope supports these types of application in a single, unified framework, providing both high run-time performance and easy application development.

There is widespread agreement that a higher level programming model for sensor networks is needed. A variety of models have been developed, but the community is far from consensus. We propose an intermediate language to speed up the exploration of this design space. Our language, called the token machine language (TML) can be targeted by compilers for higher level systems. TML provides a layer of abstraction for a lower-level runtime environment, such as TinyOS. TML is intended to capture coordinated activity in a sensor network. Notable features of TML are its atomic action model of concurrency, and its unification of communication, control, and storage around the concept of a token. Tokens are small objects, typically under a hundred bytes, and can be disseminated across the network. A token causes computation upon its arrival at a site by invoking a token handler. The effect of the computation is to atomically change the token's own state as well as the state of shared variables at the site.