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首页> 外文会议>IEEE International Parallel and Distributed Processing Symposium Workshops >Accelerating Radiation Therapy Dose Calculation with Nvidia GPUs
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Accelerating Radiation Therapy Dose Calculation with Nvidia GPUs

机译：用NVIDIA GPU加速放射治疗剂量计算

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摘要

Radiation Treatment Planning (RTP) is the process of planning the appropriate external beam radiotherapy to combat cancer in human patients. RTP is a complex and compute-intensive task, which often takes a long time (several hours) to compute. Reducing this time allows for higher productivity at clinics and more sophisticated treatment planning, which can materialize in better treatments. The state-of-the-art in medical facilities uses general-purpose processors (CPUs) to perform many steps in the RTP process. In this paper, we explore the use of accelerators to reduce RTP calculating time. We focus on the step that calculates the dose using the Graphics Processing Unit (GPU), which we believe is an excellent candidate for this computation type. Next, we create a highly optimized implementation for a custom Sparse Matrix-Vector Multiplication (SpMV) that operates on numerical formats unavailable in state-of-the-art SpMV libraries (e.g., Ginkgo and cuSPARSE). We show that our implementation is several times faster than the baseline (up-to 4x) and has a higher operational intensity than similar (but different) versions such as Ginkgo and cuSPARSE.
机译：辐射治疗计划（RTP）是规划适当的外梁放射治疗人类患者癌症的过程。 RTP是一个复杂和计算密集型任务，通常需要很长时间（几个小时）来计算。减少此时间允许在诊所和更复杂的治疗计划中提高生产率，这可以在更好的治疗中实现。医疗设备的最先进的医疗器件使用通用处理器（CPU）在RTP过程中执行许多步骤。在本文中，我们探讨了加速器的使用来减少RTP计算时间。我们专注于使用图形处理单元（GPU）计算剂量的步骤，我们认为这是该计算类型的优秀候选者。接下来，我们为自定义稀疏矩阵 - 向量乘法（SPMV）创建了一个高度优化的实现，该乘法（SPMV）在最先进的SPMV库（例如Ginkgo和Cusparse）上不可用的数字格式运行。我们表明，我们的实现比基线（最高4倍）快几倍，并且具有比类似（但不同）版本（如Ginkgo和Cusparse）更高的操作强度。

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