68 research outputs found
The East-Asian VLBI Network
The East-Asian VLBI Network (EAVN) is the international VLBI facility in East
Asia and is conducted in collaboration with China, Japan, and Korea. The EAVN
consists of VLBI arrays operated in each East Asian country, containing 21
radio telescopes and three correlators. The EAVN will be mainly operated at 6.7
(C-band), 8 (X-band), 22 (K-band), and 43 GHz (Q-band), although the EAVN has
an ability to conduct observations at 1.6 - 129 GHz. We have conducted fringe
test observations eight times to date at 8 and 22 GHz and fringes have been
successfully detected at both frequencies. We have also conducted science
commissioning observations of 6.7 GHz methanol masers in massive star-forming
regions. The EAVN will be operational from the second half of 2017, providing
complementary results with the FAST on AGNs, massive star-forming regions, and
evolved stars with high angular resolution at cm- to mm-wavelengths.Comment: 6 pages, 3 figures, 2 tables. To appear in the proceedings of
"Frontiers in Radio Astronomy and FAST Early Sciences Symposium 2015" ed. Lei
Qian (ASP Conf. Ser.
NPS: A Framework for Accurate Program Sampling Using Graph Neural Network
With the end of Moore's Law, there is a growing demand for rapid
architectural innovations in modern processors, such as RISC-V custom
extensions, to continue performance scaling. Program sampling is a crucial step
in microprocessor design, as it selects representative simulation points for
workload simulation. While SimPoint has been the de-facto approach for decades,
its limited expressiveness with Basic Block Vector (BBV) requires
time-consuming human tuning, often taking months, which impedes fast innovation
and agile hardware development. This paper introduces Neural Program Sampling
(NPS), a novel framework that learns execution embeddings using dynamic
snapshots of a Graph Neural Network. NPS deploys AssemblyNet for embedding
generation, leveraging an application's code structures and runtime states.
AssemblyNet serves as NPS's graph model and neural architecture, capturing a
program's behavior in aspects such as data computation, code path, and data
flow. AssemblyNet is trained with a data prefetch task that predicts
consecutive memory addresses.
In the experiments, NPS outperforms SimPoint by up to 63%, reducing the
average error by 38%. Additionally, NPS demonstrates strong robustness with
increased accuracy, reducing the expensive accuracy tuning overhead.
Furthermore, NPS shows higher accuracy and generality than the state-of-the-art
GNN approach in code behavior learning, enabling the generation of high-quality
execution embeddings
Mapping forests in monsoon Asia with ALOS PALSAR 50-m mosaic images and MODIS imagery in 2010.
Extensive forest changes have occurred in monsoon Asia, substantially affecting climate, carbon cycle and biodiversity. Accurate forest cover maps at fine spatial resolutions are required to qualify and quantify these effects. In this study, an algorithm was developed to map forests in 2010, with the use of structure and biomass information from the Advanced Land Observation System (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) mosaic dataset and the phenological information from MODerate Resolution Imaging Spectroradiometer (MOD13Q1 and MOD09A1) products. Our forest map (PALSARMOD50 m F/NF) was assessed through randomly selected ground truth samples from high spatial resolution images and had an overall accuracy of 95%. Total area of forests in monsoon Asia in 2010 was estimated to be ~6.3 × 10(6 )km(2). The distribution of evergreen and deciduous forests agreed reasonably well with the median Normalized Difference Vegetation Index (NDVI) in winter. PALSARMOD50 m F/NF map showed good spatial and areal agreements with selected forest maps generated by the Japan Aerospace Exploration Agency (JAXA F/NF), European Space Agency (ESA F/NF), Boston University (MCD12Q1 F/NF), Food and Agricultural Organization (FAO FRA), and University of Maryland (Landsat forests), but relatively large differences and uncertainties in tropical forests and evergreen and deciduous forests
Hermes: a Fast, Fault-Tolerant and Linearizable Replication Protocol
Today's datacenter applications are underpinned by datastores that are
responsible for providing availability, consistency, and performance. For high
availability in the presence of failures, these datastores replicate data
across several nodes. This is accomplished with the help of a reliable
replication protocol that is responsible for maintaining the replicas
strongly-consistent even when faults occur. Strong consistency is preferred to
weaker consistency models that cannot guarantee an intuitive behavior for the
clients. Furthermore, to accommodate high demand at real-time latencies,
datastores must deliver high throughput and low latency.
This work introduces Hermes, a broadcast-based reliable replication protocol
for in-memory datastores that provides both high throughput and low latency by
enabling local reads and fully-concurrent fast writes at all replicas. Hermes
couples logical timestamps with cache-coherence-inspired invalidations to
guarantee linearizability, avoid write serialization at a centralized ordering
point, resolve write conflicts locally at each replica (hence ensuring that
writes never abort) and provide fault-tolerance via replayable writes. Our
implementation of Hermes over an RDMA-enabled reliable datastore with five
replicas shows that Hermes consistently achieves higher throughput than
state-of-the-art RDMA-based reliable protocols (ZAB and CRAQ) across all write
ratios while also significantly reducing tail latency. At 5% writes, the tail
latency of Hermes is 3.6X lower than that of CRAQ and ZAB.Comment: Accepted in ASPLOS 202
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