Pagoda: A GPU runtime system for narrow tasks

Tsung Tai Yeh, Amit Sabne, Putt Sakdhnagool, Rudolf Eigenmann, Timothy G. Rogers

Research output: Contribution to journalArticlepeer-review


Massively multithreaded GPUs achieve high throughput by running thousands of threads in parallel. To fully utilize the their hardware, contemporary workloads spawn work to the GPU in bulk by launching large tasks, where each task is a kernel that contains thousands of threads that occupy the entire GPU. GPUs face severe underutilization and their performance benefits vanish if the tasks are narrow, i.e., they contain less than 512 threads. Latency-sensitive applications in network, signal, and image processing that generate a large number of tasks with relatively small inputs are examples of such limited parallelism. This article presents Pagoda, a runtime system that virtualizes GPU resources, using an OS-like daemon kernel called MasterKernel. Tasks are spawned from the CPU onto Pagoda as they become available, and are scheduled by the MasterKernel at the warp granularity. This level of control enables the GPU to keep scheduling and executing tasks as long as free warps are found, dramatically reducing underutilization. Experimental results on real hardware demonstrate that Pagoda achieves a geometric mean speedup of 5.52X over PThreads running on a 20-core CPU, 1.76X over CUDA-HyperQ, and 1.44X over GeMTC, the state-of-the-art runtime GPU task scheduling system.

Original languageEnglish
Article number21
JournalACM Transactions on Parallel Computing
Issue number4
StatePublished - Nov 2019


  • GPU runtime system
  • Task parallelism
  • Utilization

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