The ubiquity of communication devices such as smartphones has led to the emergence of context-aware services that are able to respond to specific user activities or contexts. These services allow communication providers to develop new, added-value services for a wide range of applications such as social networking, elderly care, and near-emergency early warning systems. At the core of these services is the ability to detect specific physical settings or the context a user is in, using either internal or external sensors. For example, using built-in accelerometers it is possible to determine if a user is walking or running at a specific time of day. By correlating this knowledge with GPS data it is possible to provide specific information services to users with similar daily routines. This article presents a survey of the techniques for extracting this activity information from raw accelerometer data. The techniques that can be implemented in mobile devices range from classical signal processing techniques such as FFT to contemporary string-based methods. We present experimental results to compare and evaluate the accuracy of the various techniques using real data sets collected from daily activities.

Processing-in-memory (PIM) chips that integrate processor logic into memory devices offer a new opportunity for bridging the growing gap between processor and memory speeds, especially for applications with high memory-bandwidth requirements. The Data-IntensiVe Architecture (DIVA) system combines PIM memories with one or more external host processors and a PIM-to-PIM interconnect. DIVA increases memory bandwidth through two mechanisms: (1) performing selected computation in memory, reducing the quantity of data transferred across the processor-memory interface; and (2) providing communication mechanisms called parcels for moving both data and computation throughout memory, further bypassing the processor-memory bus. DIVA uniquely supports acceleration of important irregular applications, including sparse-matrix and pointer-based computations. In this paper, we focus on several aspects of DIVA designed to effectively support such computations at very high performance levels: (1) the memory model and parcel definitions; (2) the PIM-to-PIM interconnect; and, (3) requirements for the processor-to-memory interface. We demonstrate the potential of PIMbased architectures in accelerating the performance of three irregular computations, sparse conjugate gradient, a natural-join database operation and an object-oriented database query.

We present here a report produced by a workshop on 'Addressing failures in exascale computing' held in Park City, Utah, 4-11 August 2012. The charter of this workshop was to establish a common taxonomy about resilience across all the levels in a computing system, discuss existing knowledge on resilience across the various hardware and software layers of an exascale system, and build on those results, examining potential solutions from both a hardware and software perspective and focusing on a combined approach.The workshop brought together participants with expertise in applications, system software, and hardware; they came from industry, government, and academia, and their interests ranged from theory to implementation. The combination allowed broad and comprehensive discussions and led to this document, which summarizes and builds on those discussions.

This article presents a new analysis technique, commutativity analysis, for automatically parallelizing computations that manipulate dynamic, pointer-based data structures. Commutativity analysis views the computation as composed of operations on objects. It then analyzes the program at this granularity to discover when operations commute (i.e., generate the same final result regardless of the order in which they execute). If all of the operations required to perform a given computation commute, the compiler can automatically generate parallel code. We have implemented a prototype compilation system that uses commutativity analysis as its primary analysis technique. We have used this system to automatically parallelize three complete scientific computations: the Barnes-Hut N-body solver, the Water liquid simulation code, and the String seismic simulation code. This article presents performance results for the generated parallel code running on the Stanford DASH machine. These results provide encouraging evidence that commutativity analysis can serve as the basis for a successful parallelizing compiler.