695 research outputs found
Algorithm-Directed Crash Consistence in Non-Volatile Memory for HPC
Fault tolerance is one of the major design goals for HPC. The emergence of
non-volatile memories (NVM) provides a solution to build fault tolerant HPC.
Data in NVM-based main memory are not lost when the system crashes because of
the non-volatility nature of NVM. However, because of volatile caches, data
must be logged and explicitly flushed from caches into NVM to ensure
consistence and correctness before crashes, which can cause large runtime
overhead.
In this paper, we introduce an algorithm-based method to establish crash
consistence in NVM for HPC applications. We slightly extend application data
structures or sparsely flush cache blocks, which introduce ignorable runtime
overhead. Such extension or cache flushing allows us to use algorithm knowledge
to \textit{reason} data consistence or correct inconsistent data when the
application crashes. We demonstrate the effectiveness of our method for three
algorithms, including an iterative solver, dense matrix multiplication, and
Monte-Carlo simulation. Based on comprehensive performance evaluation on a
variety of test environments, we demonstrate that our approach has very small
runtime overhead (at most 8.2\% and less than 3\% in most cases), much smaller
than that of traditional checkpoint, while having the same or less
recomputation cost.Comment: 12 page
Optimizing Guided Traversal for Fast Learned Sparse Retrieval
Recent studies show that BM25-driven dynamic index skipping can greatly
accelerate MaxScore-based document retrieval based on the learned sparse
representation derived by DeepImpact. This paper investigates the effectiveness
of such a traversal guidance strategy during top k retrieval when using other
models such as SPLADE and uniCOIL, and finds that unconstrained BM25-driven
skipping could have a visible relevance degradation when the BM25 model is not
well aligned with a learned weight model or when retrieval depth k is small.
This paper generalizes the previous work and optimizes the BM25 guided index
traversal with a two-level pruning control scheme and model alignment for fast
retrieval using a sparse representation. Although there can be a cost of
increased latency, the proposed scheme is much faster than the original
MaxScore method without BM25 guidance while retaining the relevance
effectiveness. This paper analyzes the competitiveness of this two-level
pruning scheme, and evaluates its tradeoff in ranking relevance and time
efficiency when searching several test datasets.Comment: This paper is published in WWW'2
GeneGPT: Teaching Large Language Models to Use NCBI Web APIs
In this paper, we present GeneGPT, a novel method for teaching large language
models (LLMs) to use the Web Application Programming Interfaces (APIs) of the
National Center for Biotechnology Information (NCBI) and answer genomics
questions. Specifically, we prompt Codex (code-davinci-002) to solve the
GeneTuring tests with few-shot URL requests of NCBI API calls as demonstrations
for in-context learning. During inference, we stop the decoding once a call
request is detected and make the API call with the generated URL. We then
append the raw execution results returned by NCBI APIs to the generated texts
and continue the generation until the answer is found or another API call is
detected. Our preliminary results show that GeneGPT achieves state-of-the-art
results on three out of four one-shot tasks and four out of five zero-shot
tasks in the GeneTuring dataset. Overall, GeneGPT achieves a macro-average
score of 0.76, which is much higher than retrieval-augmented LLMs such as the
New Bing (0.44), biomedical LLMs such as BioMedLM (0.08) and BioGPT (0.04), as
well as other LLMs such as GPT-3 (0.16) and ChatGPT (0.12).Comment: Work in progres
Kernel-based Substructure Exploration for Next POI Recommendation
Point-of-Interest (POI) recommendation, which benefits from the proliferation
of GPS-enabled devices and location-based social networks (LBSNs), plays an
increasingly important role in recommender systems. It aims to provide users
with the convenience to discover their interested places to visit based on
previous visits and current status. Most existing methods usually merely
leverage recurrent neural networks (RNNs) to explore sequential influences for
recommendation. Despite the effectiveness, these methods not only neglect
topological geographical influences among POIs, but also fail to model
high-order sequential substructures. To tackle the above issues, we propose a
Kernel-Based Graph Neural Network (KBGNN) for next POI recommendation, which
combines the characteristics of both geographical and sequential influences in
a collaborative way. KBGNN consists of a geographical module and a sequential
module. On the one hand, we construct a geographical graph and leverage a
message passing neural network to capture the topological geographical
influences. On the other hand, we explore high-order sequential substructures
in the user-aware sequential graph using a graph kernel neural network to
capture user preferences. Finally, a consistency learning framework is
introduced to jointly incorporate geographical and sequential information
extracted from two separate graphs. In this way, the two modules effectively
exchange knowledge to mutually enhance each other. Extensive experiments
conducted on two real-world LBSN datasets demonstrate the superior performance
of our proposed method over the state-of-the-arts. Our codes are available at
https://github.com/Fang6ang/KBGNN.Comment: Accepted by the IEEE International Conference on Data Mining (ICDM)
202
Effect of Nano-clay Filler on the Thermal Breakdown Mechanism and Lifespan of Polypropylene Film under AC Fields
The wide application of nanocomposites in the insulation system has greatly contributed to the performance improvement of power equipment. However, nano fillers are not omnipotent for improving the properties of composite dielectrics. In some situations, nano-modified materials are in fact a compromise of improving some performance features while sacrificing others. In this work, the breakdown characteristics and time-to-failure of polypropylene film with nano-clay fillers have been evaluated under combined thermal stress and AC electric fields. Experiments on plain polypropylene (PP) samples have also been carried out under the same test conditions as control. Test results indicated that the time-to-failure of the samples with nano-clay filler was shorter than those without nano filler, which is different from the previous experience. SEM and EDS analyses were conducted to study how the failure mechanism had taken place in both plain polypropylene and the nano-clay filled polypropylene. The failure phenomenon in these materials can be explained by molecular thermodynamics. The main reason for the premature thermal breakdown of PP nanocomposite is essentially due to the weak coupling between nano-clay filler and polymer matrix. Finally, suggestions are proposed for nano modification methods and lifespan prediction models of composite dielectrics
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