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 …
S Yesil, A Heidarshenas, A Morrison… - … Conference for High …, 2020 - ieeexplore.ieee.org
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 …