Data-Driven Versus Topology-driven Irregular Computations on GPUs

Abstract

Abstract-Irregular algorithms are algorithms with complex main data structures such as directed and undirected graphs, trees, etc. A useful abstraction for many irregular algorithms is its operator formulation in which the algorithm is viewed as the iterated application of an operator to certain nodes, called active nodes, in the graph. Each operator application, called an activity, usually touches only a small part of the overall graph, so nonoverlapping activities can be performed in parallel. In topologydriven implementations, all nodes are assumed to be active so the operator is applied everywhere in the graph even if there is no work to do at some nodes. In contrast, in data-driven implementations the operator is applied only to nodes at which there might be work to do. Multicore implementations of irregular algorithms are usually data-driven because current multicores only support small numbers of threads and work-efficiency is important. Conversely, many irregular GPU implementations use a topology-driven approach because work inefficiency can be counterbalanced by the large number of GPU threads.In this paper, we study data-driven and topology-driven implementations of six important graph algorithms on GPUs. Our goal is to understand the tradeoffs between these implementations and how to optimize them. We find that data-driven versions are generally faster and scale better despite the cost of maintaining a worklist. However, topology-driven versions can be superior when certain algorithmic properties are exploited to optimize the implementation. These results led us to devise hybrid approaches that combine the two techniques and outperform both of them.

Equalizer: Dynamic Tuning of GPU Resources for Efficient Execution

“…Second, resource contention is dependent on the amount of given GPU hardware resources and an application optimized for a GPU may change its point of contention on another GPU. Third, due to the prevalence of general purpose computing on GPUs (GPGPU), more irregular parallel applications are being targeted for GPUs [6,22]. This has resulted in GPGPU kernels having distinct phases where different resources are in demand.…”

Atomic-free irregular computations on GPUs

“…This kind of processing usually makes a topology-driven approach work-inefficient, but it may be suited for GPU-based processing due to the large number of available threads (cf. Nasre et al [18]). …”

Enabling and Exploiting Flexible Task Assignment on GPU through SM-Centric Program Transformations

“…As Table 1 shows, these programs come from four benchmark suites, cover a broad set of domains, and include a similar number of regular and irregular programs. Those irregular benchmarks impose special challenges for GPGPU optimization, and have drawn a lot of attention from the community recently [6,24,29,30,43,45].…”