Hewlett-Packard

About: Hewlett-Packard is a company organization based out in Palo Alto, California, United States. It is known for research contribution in the topics: Signal & Substrate (printing). The organization has 34663 authors who have published 59808 publications receiving 1467218 citations. The organization is also known as: Hewlett Packard & Hewlett-Packard Company.

Disaggregated memory for expansion and sharing in blade servers

Abstract: Analysis of technology and application trends reveals a growing imbalance in the peak compute-to-memory-capacity ratio for future servers. At the same time, the fraction contributed by memory systems to total datacenter costs and power consumption during typical usage is increasing. In response to these trends, this paper re-examines traditional compute-memory co-location on a single system and details the design of a new general-purpose architectural building block-a memory blade-that allows memory to be "disaggregated" across a system ensemble. This remote memory blade can be used for memory capacity expansion to improve performance and for sharing memory across servers to reduce provisioning and power costs. We use this memory blade building block to propose two new system architecture solutions-(1) page-swapped remote memory at the virtualization layer, and (2) block-access remote memory with support in the coherence hardware-that enable transparent memory expansion and sharing on commodity-based systems. Using simulations of a mix of enterprise benchmarks supplemented with traces from live datacenters, we demonstrate that memory disaggregation can provide substantial performance benefits (on average 10X) in memory constrained environments, while the sharing enabled by our solutions can improve performance-per-dollar by up to 57% when optimizing memory provisioning across multiple servers.

Clustering gene expression patterns

Abstract: Recent advances in biotechnology allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. Analysis of data produced by such experiments offers potential insight into gene function and regulatory mechanisms. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. The corresponding algorithmic problem is to cluster multicondition gene expression patterns. In this paper we describe a novel clustering algorithm that was developed for analysis of gene expression data. We define an appropriate stochastic error model on the input, and prove that under the conditions of the model, the algorithm recovers the cluster structure with high probability. The running time of the algorithm on an n-gene dataset is O{n2[log(n)]c}. We also present a practical heuristic based on the same algorithmic ideas. The heuristic was implemented and its performance is demonstrated on simulate...

Statistical process monitoring and feedback adjustment: a discussion

Abstract: Rationales for process monitoring using some of the techniques of statistical process control and for feedback adjustment using some techniques associated with automatic process control are explored, and issues that sometimes arise are discussed. The importance of some often unstated assumptions are illustrated. Minimum-cost feedback schemes are discussed for some simple, but practically interesting, models.

Fully integrated miniaturized planar liquid sample handling and analysis device

Abstract: A miniaturized total analysis system ("μ-TAS") comprising a miniaturized planar column device is described for use in liquid phase analysis. The μ-TAS comprises microstructures fabricated by laser ablation in a variety of novel support substrates. The μ-TAS includes associated laser-ablated features required for integrated sample analysis, such as analyte detection means and fluid communication means. μ-TAS constructed according to the invention is useful in any analysis system for detecting and analyzing small and/or macromolecular solutes in the liquid phase and may employ chromatographic separation means, electrophoretic separation means, electrochromatographic separation means, or combinations thereof.

Ensemble-level Power Management for Dense Blade Servers

Abstract: One of the key challenges for high-density servers (e.g., blades) is the increased costs in addressing the power and heat density associated with compaction. Prior approaches have mainly focused on reducing the heat generated at the level of an individual server. In contrast, this work proposes power efficiencies at a larger scale by leveraging statistical properties of concurrent resource usage across a collection of systems ("ensemble"). Specifically, we discuss an implementation of this approach at the blade enclosure level to monitor and manage the power across the individual blades in a chassis. Our approach requires low-cost hardware modifications and relatively simple software support. We evaluate our architecture through both prototyping and simulation. For workloads representing 132 servers from nine different enterprise deployments, we show significant power budget reductions at performances comparable to conventional systems.