skip to main content

Search for: All records

Creators/Authors contains: "Buhler, Jeremy"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Abstract

    Streaming dataflow applications are an attractive target to parallelize on wide-SIMD processors such as GPUs. These applications can be expressed as a pipeline of compute nodes connected by edges, which feed outputs from one node to the next. Streaming applications often exhibit irregular dataflow, where the amount of output produced for one input is unknowna priori. Inserting finite queues between pipeline nodes can ameliorate the impact of irregularity and improve SIMD lane occupancy. The sizing of these queues is driven by both performance and safety considerations- relative queue sizes should be chosen to reduce runtime overhead and maximize throughput, but each node’s output queue must be large enough to accommodate the maximum number of outputs produced by one SIMD vector of inputs to the node. When safety and performance considerations conflict, the application may incur excessive memory usage and runtime overhead. In this work, we identify properties of applications that lead to such undesirable behaviors, with examples from applications implemented in our MERCATOR framework for irregular streaming on GPUs. To address these issues, we propose extensions to supportinterruptible nodesthat can be suspended mid-execution if their output queues fill. We illustrate the impacts of adding interruptible nodes to the MERCATORmore »framework on representative irregular streaming applications from the domains of branching search and bioinformatics.

    « less
  2. Free, publicly-accessible full text available July 1, 2023
  3. Free, publicly-accessible full text available March 1, 2023
  4. Streaming computations often exhibit substantial data parallelism that makes them well-suited to SIMD architectures. However, many such computations also exhibit irregularity, in the form of data-dependent, dynamic data rates, that makes efficient SIMD execution challenging. One aspect of this challenge is the need to schedule execution of a computation realized as a pipeline of stages connected by finite queues. A scheduler must both ensure high SIMD occupancy by gathering queued items into vectors and minimize costs associated with switching execution between stages. In this work, we present the AFIE (Active Full, Inactive Empty) scheduling policy for irregular streaming applications on SIMD processors. AFIE provably groups inputs to each stage of a pipeline into a minimal number of SIMD vectors while incurring a bounded number of switches relative to the best possible policy. These results apply even though irregularity forbids a priori knowledge of how many outputs will be generated from each input to each stage. We have implemented AFIE as an extension to the MERCATOR system for building irregular streaming applications on NVIDIA GPUs. We describe how the AFIE scheduler simplifies MERCATOR’s runtime code and empirically measure the new scheduler’s improved performance on irregular streaming applications.