Modern computing systems employ significant heterogeneity and specialization to meet
performance targets at manageable power. However, memory latency bottlenecks remain …

Applications with low data reuse and frequent irregular memory accesses, such as graph or
sparse linear algebra workloads, fail to scale well due to memory bottlenecks and poor core …

The design space exploration of scaled-out manycores for communication-intensive
applications (eg, graph analytics and sparse linear algebra) is hampered due to either lack …

Recent works have introduced task-based parallelization schemes to accelerate graph
search and sparse data-structure traversal, where some solutions scale up to thousands of …

As Moore's Law is coming to an end, heterogeneous SoCs have become ubiquitous,
improving performance and efficiency with specialized hardware. However, the addition of …

Graph representations of data are ubiquitous in analytic applications. However, graph
workloads are notorious for having irregular memory access patterns with variable access …

In recent years, the growing demand to process large graphs and sparse datasets has led to
increased research efforts to develop hardware-and software-based architectural solutions …

As system parallelism at chip-and server-level increases, challenges that arose with network-
level systems a decade ago, are now being encountered with these massively parallel …

In-memory computing has long been promised as a solution to the “Memory Wall” problem.
Unfortunately, performing computations with memory resources either has relied on …

Computing systems have become ubiquitous in the modern world but their design is far from
one-size-fits-all. From battery-powered devices to supercomputers, deployment …