Simple graph algorithms such as PageRank have been the target of numerous hardware
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …

Irregular workloads are typically bottlenecked by the memory system. These workloads often
use sparse data representations, eg, compressed sparse row/column (CSR/CSC), to …

Many applications perform frequent scatter update operations to large data structures. For
example, in push-style graph algorithms, processing each vertex requires updating the data …

The Graph Neural Network (GNN) is showing outstanding results in improving the
performance of graph-based applications. Recent studies demonstrate that GNN …

Graph Pattern Mining (GPM) algorithms mine structural patterns in graphs. The performance
of GPM workloads is bottlenecked by control flow and memory stalls. This is because of data …

Graph analytics is an important workload that achieves suboptimal performance due to poor
cache locality. State-of-the-art cache replacement policies fail to capture the highly dynamic …

Irregular applications, such as graph analytics and sparse linear algebra, exhibit frequent
indirect, data-dependent accesses to single or short sequences of elements that cause high …

Graph analytics applications often target large-scale web and social networks, which are
typically power-law graphs. Graph algorithms can often be recast as generalized Sparse …

Graph processing is becoming commonplace in many applications to analyze huge
datasets. Much of the prior work in this area has assumed I/O devices with considerable …

Computation in several real-world applications such as probabilistic machine learning,
sparse linear algebra, and robotic navigation can be modeled as irregular directed acyclic …