30 research outputs found
Adaptive Routing Strategies for Modern High Performance Networks
Today’s scalable high-performance applications heavily depend on the bandwidth characteristics of their commu-nication patterns. Contemporary multi-stage interconnec-tion networks suffer from network contention which might decrease application performance. Our experiments show that the effective bisection bandwidth of a non-blocking 512-node Clos network is as low as 38 % if the network is routed statically. In this paper, we propose and ana-lyze different adaptive routing schemes for those networks. We chose Myrinet/MX to implement our proposed routing schemes. Our best adaptive routing scheme is able to in-crease the effective bisection bandwidth to 77 % for 512 nodes and 100 % for smaller node counts. Thus, we show that our proposed adaptive routing schemes are able to im-prove network throughput significantly.
Linked data query wizard: A novel interface for accessing sparql endpoints
In an interconnected world, Linked Data is more important than ever before. However, it is still quite difficult to access this new wealth of semantic data directly without having in-depth knowledge about SPARQL and related semantic technologies. Also, most people are currently used to con-suming data as 2-dimensional tables. Linked Data is by defi-nition always a graph, and not that many people are used to handle data in graph structures. Therefore we present the Linked Data Query Wizard, a web-based tool for displaying, accessing, filtering, exploring, and navigating Linked Data stored in SPARQL endpoints. The main innovation of the interface is that it turns the graph structure of Linked Data into a tabular interface and provides easy-to-use interaction possibilities by using metaphors and techniques from current search engines and spreadsheet applications that regular web users are already familiar with
RapidChiplet: A Toolchain for Rapid Design Space Exploration of Chiplet Architectures
Chiplet architectures are a promising paradigm to overcome the scaling
challenges of monolithic chips. Chiplets offer heterogeneity, modularity, and
cost-effectiveness. The design space of chiplet architectures is huge as there
are many degrees of freedom such as the number, size and placement of chiplets,
the topology of the inter-chiplet interconnect and many more. Existing tools
for cost and performance prediction are often too slow to explore this design
space. We present RapidChiplet, a fast, open-source toolchain to predict
latency and throughput of the inter-chiplet interconnect, as well as a chip's
manufacturing cost and thermal stability
Sparse Hamming Graph: A Customizable Network-on-Chip Topology
Chips with hundreds to thousands of cores require scalable networks-on-chip
(NoCs). Customization of the NoC topology is necessary to reach the diverse
design goals of different chips. We introduce sparse Hamming graph, a novel NoC
topology with an adjustable costperformance trade-off that is based on four NoC
topology design principles we identified. To efficiently customize this
topology, we develop a toolchain that leverages approximate floorplanning and
link routing to deliver fast and accurate cost and performance predictions. We
demonstrate how to use our methodology to achieve desired cost-performance
trade-offs while outperforming established topologies in cost, performance, or
both
HexaMesh: Scaling to Hundreds of Chiplets with an Optimized Chiplet Arrangement
2.5D integration is an important technique to tackle the growing cost of
manufacturing chips in advanced technology nodes. This poses the challenge of
providing high-performance inter-chiplet interconnects (ICIs). As the number of
chiplets grows to tens or hundreds, it becomes infeasible to hand-optimize
their arrangement in a way that maximizes the ICI performance. In this paper,
we propose HexaMesh, an arrangement of chiplets that outperforms a grid
arrangement both in theory (network diameter reduced by 42%; bisection
bandwidth improved by 130%) and in practice (latency reduced by 19%; throughput
improved by 34%). MexaMesh enables large-scale chiplet designs with
high-performance ICIs
Neural Graph Databases
Graph databases (GDBs) enable processing and analysis of unstructured,
complex, rich, and usually vast graph datasets. Despite the large significance
of GDBs in both academia and industry, little effort has been made into
integrating them with the predictive power of graph neural networks (GNNs). In
this work, we show how to seamlessly combine nearly any GNN model with the
computational capabilities of GDBs. For this, we observe that the majority of
these systems are based on, or support, a graph data model called the Labeled
Property Graph (LPG), where vertices and edges can have arbitrarily complex
sets of labels and properties. We then develop LPG2vec, an encoder that
transforms an arbitrary LPG dataset into a representation that can be directly
used with a broad class of GNNs, including convolutional, attentional,
message-passing, and even higher-order or spectral models. In our evaluation,
we show that the rich information represented as LPG labels and properties is
properly preserved by LPG2vec, and it increases the accuracy of predictions
regardless of the targeted learning task or the used GNN model, by up to 34%
compared to graphs with no LPG labels/properties. In general, LPG2vec enables
combining predictive power of the most powerful GNNs with the full scope of
information encoded in the LPG model, paving the way for neural graph
databases, a class of systems where the vast complexity of maintained data will
benefit from modern and future graph machine learning methods
A High-Performance Design, Implementation, Deployment, and Evaluation of The Slim Fly Network
Novel low-diameter network topologies such as Slim Fly (SF) offer significant
cost and power advantages over the established Fat Tree, Clos, or Dragonfly. To
spearhead the adoption of low-diameter networks, we design, implement, deploy,
and evaluate the first real-world SF installation. We focus on deployment,
management, and operational aspects of our test cluster with 200 servers and
carefully analyze performance. We demonstrate techniques for simple cabling and
cabling validation as well as a novel high-performance routing architecture for
InfiniBand-based low-diameter topologies. Our real-world benchmarks show SF's
strong performance for many modern workloads such as deep neural network
training, graph analytics, or linear algebra kernels. SF outperforms
non-blocking Fat Trees in scalability while offering comparable or better
performance and lower cost for large network sizes. Our work can facilitate
deploying SF while the associated (open-source) routing architecture is fully
portable and applicable to accelerate any low-diameter interconnect
Extracellular matrix protein-1 as a mediator of inflammation-induced fibrosis after myocardial infarction
Irreversible fibrosis is a hallmark of myocardial infarction (MI) and heart failure. Extracellular matrix protein-1 (ECM-1) is up-regulated in these hearts, localized to fibrotic, inflammatory, and perivascular areas. ECM-1 originates predominantly from fibroblasts, macrophages, and pericytes/vascular cells in uninjured human and mouse hearts, and from M1 and M2 macrophages and myofibroblasts after MI. ECM-1 stimulates fibroblast-to-myofibroblast transition, up-regulates key fibrotic and inflammatory pathways, and inhibits cardiac fibroblast migration. ECM-1 binds HuCFb cell surface receptor LRP1, and LRP1 inhibition blocks ECM-1 from stimulating fibroblast-to-myofibroblast transition, confirming a novel ECM-1-LRP1 fibrotic signaling axis. ECM-1 may represent a novel mechanism facilitating inflammation-fibrosis crosstalk
Learning With Social Semantic Technologies - Exploiting Latest Tools
Even though it was only about three years ago that Social Software became a trend, it has become a common practice to utilize Social Software in learning institutions. It brought about a lot of advantages, but also challenges. Amounts of distributed and often unstructured user generated content make it difficult to meaningfully process and find relevant information. According to the estimate of the authors, the solution lies in underpinning Social Software with structure resulting in Social Semantic Software. In this contribution we introduce the central concepts Social Software, Semantic Web and Social Semantic Web and show how Social Semantic Technologies might be utilized in the higher education context