5,938 research outputs found
Distributed Graph Embedding with Information-Oriented Random Walks
Graph embedding maps graph nodes to low-dimensional vectors, and is widely
adopted in machine learning tasks. The increasing availability of billion-edge
graphs underscores the importance of learning efficient and effective
embeddings on large graphs, such as link prediction on Twitter with over one
billion edges. Most existing graph embedding methods fall short of reaching
high data scalability. In this paper, we present a general-purpose,
distributed, information-centric random walk-based graph embedding framework,
DistGER, which can scale to embed billion-edge graphs. DistGER incrementally
computes information-centric random walks. It further leverages a
multi-proximity-aware, streaming, parallel graph partitioning strategy,
simultaneously achieving high local partition quality and excellent workload
balancing across machines. DistGER also improves the distributed Skip-Gram
learning model to generate node embeddings by optimizing the access locality,
CPU throughput, and synchronization efficiency. Experiments on real-world
graphs demonstrate that compared to state-of-the-art distributed graph
embedding frameworks, including KnightKing, DistDGL, and Pytorch-BigGraph,
DistGER exhibits 2.33x-129x acceleration, 45% reduction in cross-machines
communication, and > 10% effectiveness improvement in downstream tasks
LASAGNE: Locality And Structure Aware Graph Node Embedding
In this work we propose Lasagne, a methodology to learn locality and
structure aware graph node embeddings in an unsupervised way. In particular, we
show that the performance of existing random-walk based approaches depends
strongly on the structural properties of the graph, e.g., the size of the
graph, whether the graph has a flat or upward-sloping Network Community Profile
(NCP), whether the graph is expander-like, whether the classes of interest are
more k-core-like or more peripheral, etc. For larger graphs with flat NCPs that
are strongly expander-like, existing methods lead to random walks that expand
rapidly, touching many dissimilar nodes, thereby leading to lower-quality
vector representations that are less useful for downstream tasks. Rather than
relying on global random walks or neighbors within fixed hop distances, Lasagne
exploits strongly local Approximate Personalized PageRank stationary
distributions to more precisely engineer local information into node
embeddings. This leads, in particular, to more meaningful and more useful
vector representations of nodes in poorly-structured graphs. We show that
Lasagne leads to significant improvement in downstream multi-label
classification for larger graphs with flat NCPs, that it is comparable for
smaller graphs with upward-sloping NCPs, and that is comparable to existing
methods for link prediction tasks
Near-Memory Address Translation
Memory and logic integration on the same chip is becoming increasingly cost
effective, creating the opportunity to offload data-intensive functionality to
processing units placed inside memory chips. The introduction of memory-side
processing units (MPUs) into conventional systems faces virtual memory as the
first big showstopper: without efficient hardware support for address
translation MPUs have highly limited applicability. Unfortunately, conventional
translation mechanisms fall short of providing fast translations as
contemporary memories exceed the reach of TLBs, making expensive page walks
common.
In this paper, we are the first to show that the historically important
flexibility to map any virtual page to any page frame is unnecessary in today's
servers. We find that while limiting the associativity of the
virtual-to-physical mapping incurs no penalty, it can break the
translate-then-fetch serialization if combined with careful data placement in
the MPU's memory, allowing for translation and data fetch to proceed
independently and in parallel. We propose the Distributed Inverted Page Table
(DIPTA), a near-memory structure in which the smallest memory partition keeps
the translation information for its data share, ensuring that the translation
completes together with the data fetch. DIPTA completely eliminates the
performance overhead of translation, achieving speedups of up to 3.81x and
2.13x over conventional translation using 4KB and 1GB pages respectively.Comment: 15 pages, 9 figure
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