Systems and methods for navigating hypermedia using multiple coordinated input/output device sets. Disclosed systems and methods allow a user and/or an author to control what resources are presented on which device sets (whether they are integrated or not), and provide for coordinating browsing activities to enable such a user interface to be employed across multiple independent systems. Disclosed systems and methods also support new and enriched aspects and applications of hypermedia browsing and related business activities.

Method and apparatus for browsing using multiple coordinated device sets

Method and Apparatus for Browsing Using Alternative Linkbases

Transactional Memory (TM) is emerging as a promising technology to simplify parallel programming. While several TM systems have been proposed in the research literature, we are still missing the tools and workloads necessary to analyze and compare the proposals. Most TM systems have been evaluated using microbenchmarks, which may not be representative of any real-world behavior, or individual applications, which do not stress a wide range of execution scenarios. We introduce the Stanford Transactional Application for Multi-Processing (STAMP), a comprehensive benchmark suite for evaluating TM systems. STAMP includes eight applications and thirty variants of input parameters and data sets in order to represent several application domains and cover a wide range of transactional execution cases (frequent or rare use of transactions, large or small transactions, high or low contention, etc.). Moreover, STAMP is portable across many types of TM systems, including hardware, software, and hybrid systems. In this paper, we provide descriptions and a detailed characterization of the applications in STAMP. We also use the suite to evaluate six different TM systems, identify their shortcomings, and motivate further research on their performance characteristics.

/pdf/stamp-stanford-transactional-applications-for-multi-5ascbp3x5h.pdf

STAMP: Stanford Transactional Applications for Multi-Processing

: Current systems for graph computation require a distributed computing cluster to handle very large real-world problems, such as analysis on social networks or the web graph. While distributed computational resources have become more accessible developing distributed graph algorithms still remains challenging, especially to non-experts. In this work, we present GraphChi, a disk-based system for computing efficiently on graphs with billions of edges. By using a well-known method to break large graphs into small parts, and a novel Parallel Sliding Windows algorithm, GraphChi is able to execute several advanced data mining, graph mining and machine learning algorithms on very large graphs, using just a single consumer-level computer. We show, through experiments and theoretical analysis, that GraphChi performs well on both SSDs and rotational hard drives. We build on the basis of Parallel Sliding Windows to propose a new data structure Partitioned Adjacency Lists, which we use to design an online graph database GraphChi-DB.We demonstrate that, on a single PC, GraphChi-DB can process over one hundred thousand graph updates per second, while simultaneously performing computation. GraphChi-DB compares favorably to existing graph databases, particularly on data that is much larger than the available memory. We evaluate our work both experimentally and theoretically. Based on the Parallel Sliding Windows algorithm, we propose new I/O efficient algorithms for solving fundamental graph problems. We also propose a novel algorithm for simulating billions of random walks in parallel on a single computer. By repeating experiments reported for existing distributed systems we show that with only fraction of the resources, GraphChi can solve the same problems in a very reasonable time. Our work makes large-scale graph computation available to anyone with a modern PC.

Large-scale Graph Computation on Just a PC

Memcached is a well known, simple, in-memory caching solution. This paper describes how Facebook leverages memcached as a building block to construct and scale a distributed key-value store that supports the world's largest social network. Our system handles billions of requests per second and holds trillions of items to deliver a rich experience for over a billion users around the world.

/pdf/scaling-memcache-at-facebook-1k76zb515n.pdf

Scaling Memcache at Facebook

We introduce a simple data model and API tailored for serving the social graph, and TAO, an implementation of this model. TAO is a geographically distributed data store that provides efficient and timely access to the social graph for Facebook's demanding workload using a fixed set of queries. It is deployed at Facebook, replacing memcache for many data types that fit its model. The system runs on thousands of machines, is widely distributed, and provides access to many petabytes of data. TAO can process a billion reads and millions of writes each second.

/pdf/tao-facebook-s-distributed-data-store-for-the-social-graph-1ufeq8hrnk.pdf

TAO: Facebook's distributed data store for the social graph

We propose signature-accelerated transactional memory (SigTM), ahybrid TM system that reduces the overhead of software transactions. SigTM uses hardware signatures to track the read-set and write-set forpending transactions and perform conflict detection between concurrent threads. All other transactional functionality, including dataversioning, is implemented in software. Unlike previously proposed hybrid TM systems, SigTM requires no modifications to the hardware caches, which reduces hardware cost and simplifies support for nested transactions and multithreaded processor cores. SigTM is also the first hybrid TM system to provide strong isolation guarantees between transactional blocks and non-transactional accesses without additional read and write barriers in non-transactional code.Using a set of parallel programs that make frequent use of coarse-grain transactions, we show that SigTM accelerates software transactions by 30% to 280%. For certain workloads, SigTM can match the performance of a full-featured hardware TM system, while for workloads with large read-sets it can be up to two times slower. Overall, we show that SigTM combines the performance characteristics and strong isolation guarantees of hardware TM implementations with the low cost and flexibility of software TM systems.

/pdf/an-effective-hybrid-transactional-memory-system-with-strong-3ggostche4.pdf

An effective hybrid transactional memory system with strong isolation guarantees

Methods, systems, and computer readable media for facilitating trading two items (L,Q) from the group of items comprising commodities and financial instruments. At least two agents ( 2 ) want to trade the items (L,Q). There is a trading channel ( 3 ) between the two agents ( 2 ) allowing for the execution of trades. There are flow (trading) limits on the items being traded. A central computer ( 1 ) is coupled to the two agents ( 2 ). Said computer ( 1 ) is adapted to convey to each agent ( 2 ) current tradable bid and offered prices and sizes subject to the agent's ( 2 ) trading limits.

Global electronic trading system

We address the problem of testing atomicity of composed concurrent operations. Concurrent libraries help programmers exploit parallel hardware by providing scalable concurrent operations with the illusion that each operation is executed atomically. However, client code often needs to compose atomic operations in such a way that the resulting composite operation is also atomic while preserving scalability. We present a novel technique for testing the atomicity of client code composing scalable concurrent operations. The challenge in testing this kind of client code is that a bug may occur very rarely and only on a particular interleaving with a specific thread configuration. Our technique is based on modular testing of client code in the presence of an adversarial environment; we use commutativity specifications to drastically reduce the number of executions explored to detect a bug. We implemented our approach in a tool called COLT, and evaluated its effectiveness on a range of 51 real-world concurrent Java programs. Using COLT, we found 56 atomicity violations in Apache Tomcat, Cassandra, MyFaces Trinidad, and other applications.

/pdf/testing-atomicity-of-composed-concurrent-operations-1qtf6cn576.pdf

Testing atomicity of composed concurrent operations

We describe the design, implementation, and evaluation of Occult (Observable Causal Consistency Using Lossy Timestamps), the first scalable, geo-replicated data store that provides causal consistency to its clients without exposing the system to the possibility of slowdown cascades, a key obstacle to the deployment of causal consistency at scale. Occult supports read/write transactions under PC-PSI, a variant of Parallel Snapshot Isolation that contributes to Occult's immunity to slowdown cascades by weakening how PSI replicates transactions committed at the same replica. While PSI insists that they all be totally ordered, PC-PSI simply requires total order Per Client session. Nonetheless, Occult guarantees that all transactions read from a causally consistent snapshot of the datastore without requiring any coordination in how transactions are asynchronously replicated.

/pdf/i-can-t-believe-it-s-not-causal-scalable-causal-consistency-12t8beom4b.pdf

Nathan Bronson

Papers

TAO: Facebook's distributed data store for the social graph

An effective hybrid transactional memory system with strong isolation guarantees

Global electronic trading system

Testing atomicity of composed concurrent operations

I can't believe it's not causal! scalable causal consistency with no slowdown cascades