Physical Sciences and Mathematics Commons^™

Embedding Parallel Computation In A Stochastic Mesh Network: A Morphogenetic Approach, Max Orhai

Anthós

Many basic techniques in computer science have been founded on the assumption that physical computing resources are scarce but orderly, and that the cost of effective direct communication between physically distant parts of a computer system is affordable. In ubiquitous computing systems such as sensor networks, or in the design of nano-scale systems, these familiar assumptions may not hold.

What if we suppose instead that computing capacity is plentiful, but that only local communication is possible, and the exact structure of the communication network is not known in advance? This is the domain of spatial programming.

How can we program …

Resizable, Scalable, Concurrent Hash Tables, Josh Triplett, Paul E. Mckenney, Jonathan Walpole

Computer Science Faculty Publications and Presentations

We present algorithms for shrinking and expanding a hash table while allowing concurrent, wait-free, linearly scalable lookups. These resize algorithms allow the hash table to maintain constant-time performance as the number of entries grows, and reclaim memory as the number of entries decreases, without delaying or disrupting readers.

We implemented our algorithms in the Linux kernel, to test their performance and scalability. Benchmarks show lookup scalability improved 125x over readerwriter locking, and 56% over the current state-of-the-art for Linux, with no performance degradation for lookups during a resize.

To achieve this performance, this hash table implementation uses a new concurrent …

Resizable, Scalable, Concurrent Hash Tables Via Relativistic Programming, Josh Triplett, Paul E. Mckenney, Jonathan Walpole

Computer Science Faculty Publications and Presentations

Presentation focusing on software synchronization, thread locking, transactional memory, and relativistic programming. Hash table algorithms are presented with examples of relativistic list insertion and removal, and related data structures. Existing approaches are compared to new methodologies and future work with relativistic data structures.

Hardware Acceleration Of Inference Computing: The Numenta Htm Algorithm, Dan Hammerstrom

Systems Science Friday Noon Seminar Series

In this presentation I will describe the latest version of the Numenta HTM Cortical Learning Algorithm and why it is interesting for doing research into radical new computer architectures. Then I will discuss the hardware acceleration research we are doing, and briefly look at some preliminary applications development.

A Relativistic Enhancement To Software Transactional Memory, Philip William Howard, Jonathan Walpole

Computer Science Faculty Publications and Presentations

Relativistic Programming is a technique that allows low overhead, linearly-scalable concurrent reads. It also allows joint access parallelism between readers and a writer. Unfortunately, it has so far been limited to a single writer so it does not scale on the write side. Software Transactional Memory (STM) is a technique that allows programs to take advantage of disjoint access parallelism on both the read-side and write-side. Unfortunately, STM systems have a higher overhead than many other synchronization mechanisms so although STM scales, STM starts from a lower baseline. We propose combining relativistic programming and software transactional memory in a way …

Higher-Level Application Of Adaptive Dynamic Programming/Reinforcement Learning – A Next Phase For Controls And System Identification?, George G. Lendaris

Systems Science Friday Noon Seminar Series

Humans have the ability to make use of experience while performing system identification and selecting control actions for changing situations. In contrast to current technological implementations that slow down as more knowledge is stored, as more experience is gained, human processing speeds up and has enhanced effectiveness. An emerging experience-based (“higher level”) approach promises to endow our technology with enhanced efficiency and effectiveness.

The notions of context and context discernment are important to understanding this human ability. These are defined as appropriate to controls and system-identification. Some general background on controls, Dynamic Programming, and Adaptive Critic leading to Adaptive Dynamic …

Scalable Correct Memory Ordering Via Relativistic Programming, Josh Triplett, Philip William Howard, Paul E. Mckenney, Jonathan Walpole

Computer Science Faculty Publications and Presentations

We propose and document a new concurrent programming model, relativistic programming. This model allows readers to run concurrently with writers, without blocking or using expensive synchronization. Relativistic programming builds on existing synchronization primitives that allow writers to wait for current readers to finish with minimal reader overhead. Our methodology models data structures as graphs, and reader algorithms as traversals of these graphs; from this foundation we show how writers can implement arbitrarily strong ordering guarantees for the visibility of their writes, up to and including total ordering.

Generalized Construction Of Scalable Concurrent Data Structures Via Relativistic Programming, Josh Triplett, Paul E. Mckenney, Philip W. Howard, Jonathan Walpole

Computer Science Faculty Publications and Presentations

We present relativistic programming, a concurrent programming model based on shared addressing, which supports efficient, scalable operation on either uniform shared-memory or distributed shared- memory systems. Relativistic programming provides a strong causal ordering property, allowing a series of read operations to appear as an atomic transaction that occurs entirely between two ordered write operations. This preserves the simple immutable-memory programming model available via mutual exclusion or transactional memory. Furthermore, relativistic programming provides joint-access parallelism, allowing readers to run concurrently with a writer on the same data. We demonstrate a generalized construction technique for concurrent data structures based on relativistic programming, …

A Comparison Of Relativistic And Reader-Writer Locking Approaches To Shared Data Access, Philip William Howard, Josh Triplett, Jonathan Walpole

Computer Science Faculty Publications and Presentations

This paper explores the relationship between reader-writer locking and relativistic programming approaches to managing accesses to shared data. It demonstrates that by placing certain restrictions on writers, relativistic programming allows more concurrency than reader-writer locking while still providing the same isolation guarantees. Relativistic programming also allows for a straightforward model for reasoning about the correctness of programs that allow concurrent read-write accesses.

On The Effect Of Criticality And Topology On Learning In Random Boolean Networks, Alireza Goudarzi

Systems Science Friday Noon Seminar Series

Random Boolean networks (RBN) are discrete dynamical systems composed of N automata with a binary state, each of which interacts with other automata in the network. RBNs were originally introduced as simplified models of gene regulation. In this presentation, I will present recent work done conjointly with Natali Gulbahce (UCSF), Thimo Rohlf (MPI, CNRS), and Christof Teuscher (PSU). We extend the study of learning in feedforward Boolean networks to random Boolean networks (RBNs) and systematically explore the relationship between the learning capability, the network topology, the system size N, the training sample T, and the complexity of the computational task. …

Floorplan Design And Yield Enhancement Of 3-D Integrated Circuits, Rajeev Kumar Nain

Dissertations and Theses

We have developed a placement-aware 3-D floorplanning algorithm that enables additional wirelength reduction by planning for 3-D placement of logic gates in selected circuit modules during the floorplanning stage. Thus it also bridges the existing gap between 3-D floorplanning and 3-D placement. To reduce the solution space of 3-D floorplanning which is known to be an NP-hard problem, we derive a set of feasibility conditions on the topological representation of a floorplan. In addition, we have designed a fast module packing algorithm that satisfies a set of constraints for placement-aware 3-D floorplanning. Furthermore, we have designed an efficient evolutionary algorithm …

Can Transportation Researchers Reuse Project Datasets And Programs?, Tyler Hayes, Lois Delcambre, Leonard Shapiro

Computer Science Faculty Publications and Presentations

Data users involved in research and analysis typically invest a lot of eort cleaning and manipu- lating their data as they work. Based on this observation, we have investigated two hypotheses: 1) reuse of datasets and procedures is difficult, and 2) the inability to reuse datasets and procedures is primarily due to a lack of documentation. To test these hypotheses we conducted structured interviews with data users asking questions regarding the struggles in their work pertaining to data, their documentation habits, and the importance of documentation. The interviews revealed that the data users rarely reused data or procedures, frequently encountered …

Relativistic Red-Black Trees, Philip William Howard, Jonathan Walpole

Computer Science Faculty Publications and Presentations

Operating system performance and scalability on sharedmemory many-core systems depends critically on efficient access to shared data structures. Scalability has proven difficult to achieve for many data structures. In this paper we present a novel and highly scalable concurrent red-black tree. Red-black trees are widely used in operating systems, but typically exhibit poor scalability. Our red-black tree has linear read scalability, uncontended read performance that is at least 25% faster than other known approaches, and deterministic lookup times for a given tree size, making it suitable for realtime applications.

The Ordering Requirements Of Relativistic And Reader-Writer Locking Approaches To Shared Data Access, Philip William Howard, Josh Triplett, Jonathan Walpole, Paul E. Mckenney

Computer Science Faculty Publications and Presentations

The semantics of reader-writer locks allow read-side concurrency. Unfortunately, the locking primitives serialize access to the lock variable to an extent that little or no concurrency is realized in practice for small critical sections. Relativistic programming is a methodology that also allows read- side concurrency. Relativistic programming uses dfferent ordering constraints than reader-writer locking. The different ordering constraints allow relativistic readers to proceed without synchronization so relativistic readers scale even for very short critical sections. In this paper we explore the diferences between the ordering constraints for reader-writer locking and relativistic programs. We show how and why the dfferent ordering …

Haskell For The Cloud, Andrew P. Black

Computer Science Faculty Publications and Presentations

We present Cloud Haskell, a domain specific language for developing programs for a distributed-memory computing environment. Implemented as a shallow embedding in Haskell, it provides a message-passing communication model, inspired by Erlang, without introducing incompatibility with Haskell's established sharedmemory concurrency. A key contribution is a method for serializing function closures for transmission across the network. Cloud Haskell has been implemented; we present example code and some preliminary performance measurements.

Finding Haystacks With Needles: Ranked Search For Data Using Geospatial And Temporal Characteristics, Veronika Margaret Megler, David Maier

Computer Science Faculty Publications and Presentations

The past decade has seen an explosion in the number and types of environmental sensors deployed, many of which provide a continuous stream of observations. Each individual observation consists of one or more sensor measurements, a geographic location, and a time. With billions of historical observations stored in diverse databases and in thousands of datasets, scientists have difficulty finding relevant observations. We present an approach that creates consistent geospatial-temporal metadata from large repositories of diverse data by blending curated and automated extracts. We describe a novel query method over this metadata that returns ranked search results to a query with …