Recently, Graph Neural Networks (GNNs) have been receiving a spotlight as a powerful tool
that can effectively serve various inference tasks on graph structured data. As the size of real …

Many solutions have been recently proposed to support the processing of streaming graphs.
However, for the processing of each graph snapshot of a streaming graph, the new states of …

Current systems hide data movement from software behind the load-store interface.
Software's inability to observe and respond to data movement is the root cause of many …

Sparse matrix multiplication is one of the key computational kernels in large-scale data
analytics. However, a naive implementation suffers from the overheads of irregular memory …

Performance in scientific and engineering applications such as computational physics,
algebraic graph problems or Convolutional Neural Networks (CNN), is dominated by the …

To alleviate the performance and energy overheads of contemporary applications with large
data footprints, we propose the Two Level Perceptron (TLP) predictor, a neural mechanism …

Cache Replacement Policies are known to have an important impact on hit rates. The OPT
replacement policy [27] has been formally proven as optimal for minimizing misses. Due to …

Improving cache performance is a lasting research topic. While utilizing data locality to
enhance cache performance becomes more and more difficult, data access concurrency …

In modern data centers, massive concurrent graph processing jobs are being processed on
large graphs. However, existing hardware/-software solutions suffer from irregular graph …

Despite state-of-the-art caching strategies, graph analytics pose a significant challenge for
prefetching and replacement policies, as their access patterns are often random with low …