Taskflow: A General-Purpose Parallel and Heterogeneous Task Programming System

Huang, Tsung‐Wei; Lin, Dian-Lun; Lin, Yibo; Lin, Chun-Xun

doi:10.1109/tcad.2021.3082507

Cited by 19 publications

(1 citation statement)

References 23 publications

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“…a method reduces the amount of GPU memory consumption, we must carefully orchestrate the three CUDA streams to avoid resource conflicts because now the streams share the same working buffer. Task graph based methods [26,27] can be used to schedule the GPU kernels to prevent resource conflicts, considering inter-stream dependencies. Figure 7(a) illustrates the directed acyclic graph (DAG) of an out-of-core GPU stencil computation code using compression and the proposed single working buffer method.…”

Section: Single Working Buffer Techniquementioning

confidence: 99%

Compression-Based Optimizations for Out-of-Core GPU Stencil Computation

Shen¹,

Deng²,

Wu³

et al. 2022

Preprint

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An out-of-core stencil computation code handles large data whose size is beyond the capacity of GPU memory. Whereas, such an code requires streaming data to and from the GPU frequently. As a result, data movement between the CPU and GPU usually limits the performance. In this work, compression-based optimizations are proposed. First, an on-the-fly compression technique is applied to an out-of-core stencil code, reducing the CPU-GPU memory copy. Secondly, a single working buffer technique is used to reduce GPU memory consumption. Experimental results show that the stencil code using the proposed techniques achieved 1.1× speed and reduced GPU memory consumption by 33.0% on an NVIDIA Tesla V100 GPU.

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Section: Single Working Buffer Techniquementioning

confidence: 99%