481 research outputs found
Toward sustainable data centers: a comprehensive energy management strategy
Data centers are major contributors to the emission of carbon dioxide to the atmosphere, and this contribution is expected to increase in the following years. This has encouraged the development of techniques to reduce the energy consumption and the environmental footprint of data centers. Whereas some of these techniques have succeeded to reduce the energy consumption of the hardware equipment of data centers (including IT, cooling, and power supply systems), we claim that sustainable data centers will be only possible if the problem is faced by means of a holistic approach that includes not only the aforementioned techniques but also intelligent and unifying solutions that enable a synergistic and energy-aware management of data centers.
In this paper, we propose a comprehensive strategy to reduce the carbon footprint of data centers that uses the energy as a driver of their management procedures. In addition, we present a holistic management architecture for sustainable data centers that implements the aforementioned strategy, and we propose design guidelines to accomplish each step of the proposed strategy, referring to related achievements and enumerating the main challenges that must be still solved.Peer ReviewedPostprint (author's final draft
HiHGNN: Accelerating HGNNs through Parallelism and Data Reusability Exploitation
Heterogeneous graph neural networks (HGNNs) have emerged as powerful
algorithms for processing heterogeneous graphs (HetGs), widely used in many
critical fields. To capture both structural and semantic information in HetGs,
HGNNs first aggregate the neighboring feature vectors for each vertex in each
semantic graph and then fuse the aggregated results across all semantic graphs
for each vertex. Unfortunately, existing graph neural network accelerators are
ill-suited to accelerate HGNNs. This is because they fail to efficiently tackle
the specific execution patterns and exploit the high-degree parallelism as well
as data reusability inside and across the processing of semantic graphs in
HGNNs.
In this work, we first quantitatively characterize a set of representative
HGNN models on GPU to disclose the execution bound of each stage,
inter-semantic-graph parallelism, and inter-semantic-graph data reusability in
HGNNs. Guided by our findings, we propose a high-performance HGNN accelerator,
HiHGNN, to alleviate the execution bound and exploit the newfound parallelism
and data reusability in HGNNs. Specifically, we first propose a bound-aware
stage-fusion methodology that tailors to HGNN acceleration, to fuse and
pipeline the execution stages being aware of their execution bounds. Second, we
design an independency-aware parallel execution design to exploit the
inter-semantic-graph parallelism. Finally, we present a similarity-aware
execution scheduling to exploit the inter-semantic-graph data reusability.
Compared to the state-of-the-art software framework running on NVIDIA GPU T4
and GPU A100, HiHGNN respectively achieves an average 41.5 and
8.6 speedup as well as 106 and 73 energy efficiency
with quarter the memory bandwidth of GPU A100
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