With the releases of Alexa Voice Services and Google Home, voice-driven interactive computing has quickly become commonplace. Voice interactive applications incorporate multiple components including complex speech recognition and translation algorithms, natural language understanding and generation capabilities, as well as custom compute functions commonly referred to as skills. Voice-driven interactive systems are composed of software pipelines using these components. These pipelines are typically resource intensive and must be executed quickly to maintain dialogue-consistent latency; consequently, voice interaction pipelines are usually computed in the cloud. However, for many cases, cloud connectivity may not be practical and thus require these voice interactive pipelines be executed at the edge.

Rough set theory is a popular and powerful machine learning tool. It is especially suitable for dealing with information systems that exhibit inconsistencies, i.e. objects that have the same values for the conditional attributes but a different value for the decision attribute. In line with the emerging granular computing paradigm, rough set theory groups objects together based on the indiscernibility of their attribute values. Fuzzy rough set theory extends rough set theory to data with continuous attributes, and detects degrees of inconsistency in the data. Key to this is turning the indiscernibility relation into a gradual relation, acknowledging that objects can be similar to a certain extent. In very large datasets with millions of objects, computing the gradual indiscernibility relation (or in other words, the soft granules) is very demanding, both in terms of runtime and in terms of memory. It is however required for the computation of the lower and upper approximations of concepts in the fuzzy rough set analysis pipeline. Current non-distributed implementations in R are limited by memory capacity. For example, we found that a state of the art non-distributed implementation in R could not handle 30,000 rows and 10 attributes on a node with 62GB of memory. This is clearly insufficient to scale fuzzy rough set analysis to massive datasets. In this paper we present a parallel and distributed solution based on Message Passing Interface (MPI) to compute fuzzy rough approximations in very large information systems. Our results show that our parallel approach scales with problem size to information systems with millions of objects. To the best of our knowledge, no other parallel and distributed solutions have been proposed so far in the literature for this problem.

Main memory power in volume and mid-range servers is growing as a fraction of total system power. The resulting energy consumption increases system cost and the heat produced reduces reliability. Emergent memory technology will provide systems with the ability to dynamically turn-on (online) and turn-off (offline) memory devices at runtime. This technology, coupled with slack in memory demand, offers the potential for significant energy savings in servers. However, to gain general acceptance in the server community, power-aware techniques must maintain performance and scale to thousands of memory devices. We propose a memory management infra-structure for energy reduction (Memory MISER) that is transparent, performance-neutral, and scalable. Memory MISER provides: 1) a prototype Linux kernel that manages memory at device granularity, and 2) a user space daemon that tracks systemic memory demand and implements energy- and performance-constrained device controller policies. Experiments on an 8-node cluster of servers show our Memory MISER conserves memory energy up to 56.8 percent with no performance degradation for scientific codes that utilized the entire cluster. For multi-user workloads, we achieved memory energy savings of up to 67.94 percent with no performance degradation. Normalizing to total system energy consumption, our power-aware memory approach reduced energy between 18.81 percent and 39.02 percent.

Main memory in many tera-scale systems requires tens of kilowatts of power. The resulting energy consumption increases system cost and the heat produced reduces reliability. Emergent memory technologies will provide systems the ability to dynamically turn-on (online) and turn-off (offline) memory devices at runtime. This technology, coupled with slack in memory demand, offers the potential for significant energy savings in clusters of servers. However, to realize these energy savings, OS-level memory allocation and management techniques must be modified to minimize the number of active memory devices while satisfying application demands. We propose several page shaping techniques and structural enhancements to proactively and reactively direct allocations to a minimal number of devices. To evaluate these techniques on real systems, we implemented these shaping techniques in the Linux kernel. Experiments using our OS extensions coupled with a simple history-based heuristic (to track demand and control state transitions) yield up to 60% energy savings with less than 1% performance loss for various benchmarks including lmbench and SPEC.

Total system energy is often dominated by microprocessor power consumption. However, the need for increased memory capacity in high-performance server systems requires integrated support for additional memory devices. Moore's Law implies these devices will achieve higher densities at the expense of power and energy.