Heterogeneous CPU-GPU Epsilon Grid Joins: Static and Dynamic Work Partitioning Strategies

Adaptive Partitioning for Irregular Applications on Heterogeneous CPU-GPU Chips

Commodity processors are comprised of several CPU cores and one integrated GPU. To fully exploit this type of architectures, one needs to automatically determine how to partition the workload between both devices. This is specially challenging for irregular workloads, where each iteration’s work is data dependent and shows control and memory divergence. In this paper, we present a novel adaptiv...

Runtime specialization for heterogeneous CPU-GPU platforms

Cache optimization for CPU - GPU heterogeneous processors ∗

Microprocessors combining CPU and GPU cores using a common last-level cache pose new challenges to cache management algorithms. Since GPU cores feature much higher data access rates than CPU cores, the majority of the available cache space will be used by GPU applications, leaving only very limited cache capacity for CPU applications, which may be disadvantageous for overall system performance....

Hierarchical Partitioning Algorithm for Scientific Computing on Highly Heterogeneous CPU + GPU Clusters

Hierarchical level of heterogeneity exists in many modern high performance clusters in the form of heterogeneity between computing nodes, and within a node with the addition of specialized accelerators, such as GPUs. To achieve high performance of scientific applications on these platforms it is necessary to perform load balancing. In this paper we present a hierarchical matrix partitioning alg...

Static and Dynamic Partitioning of

Identifying some pointers as invisible threads, for the purposes of storage management, is a generalization from several widely used programming conventions, like threaded trees. The necessary invariant is that nodes that are accessible (without threads) emit threads only to other accessible nodes. Dynamic tagging or static typing of threads ameliorates storage recycling both in functional and ...