With increasing core-count, the cache demand of modern processors has also increased.
However, due to strict area/power budgets and presence of poor data-locality workloads …

Originally developed to support video games, graphics processor units (GPUs) are now
increasingly used for general-purpose (non-graphics) applications ranging from machine …

Graphics Processing Units (GPUs) exploit large amounts of threadlevel parallelism to
provide high instruction throughput and to efficiently hide long-latency stalls. The resulting …

This paper presents novel cache optimizations for massively parallel, throughput-oriented
architectures like GPUs. L1 data caches (L1 D-caches) are critical resources for providing …

The ubiquity of graphics processing unit (GPU) architectures has made them efficient
alternatives to chip-multiprocessors for parallel workloads. GPUs achieve superior …

Cache is designed to exploit locality; however, the role of on-chip L1 data caches on modern
GPUs is often awkward. The locality among global memory requests from different SMs …

Graph pattern mining (GPM) is a class of algorithms widely used in many real-world
applications in bio-medicine, e-commerce, security, social sciences, etc. GPM is a …

A major issue faced by data scientists today is how to scale up their processing infrastructure
to meet the challenge of big data and high‐performance computing (HPC) workloads. With …

Long latency of memory operation is a prominent performance bottleneck in graphics
processing units (GPUs). The small data cache that must be shared across dozens of warps …

Exploiting data locality in GPUs is critical to making more efficient use of the existing caches
and the NUMA-based memory hierarchy expected in future GPUs. While modern GPU …