标题： 近似顶部-$k$以增加并行性 显示英文标题

标题： Approximate Top-$k$ for Increased Parallelism

摘要： 我们对分桶近似top-$k$算法进行评估。 计算top-$k$的精确值受到并行性的限制，因为必须沿向量聚合$k$个最大值，因此不适合在高度并行的机器学习加速器上进行计算。 通过放松对top-$k$必须精确的要求，分桶算法可以通过独立计算许多较小的top-$k$操作来显著增加可用的并行性。 我们通过理论分析和下游任务的实证评估来探索这类算法的设计选择。 我们的动机示例是语言模型中的稀疏性算法，这些算法通常使用top-$k$来选择最重要的参数或激活值。 我们还为PyTorch发布了一个快速的分桶top-$k$实现。 显示英文摘要

摘要： We present an evaluation of bucketed approximate top-$k$ algorithms. Computing top-$k$ exactly suffers from limited parallelism, because the $k$ largest values must be aggregated along the vector, thus is not well suited to computation on highly-parallel machine learning accelerators. By relaxing the requirement that the top-$k$ is exact, bucketed algorithms can dramatically increase the parallelism available by independently computing many smaller top-$k$ operations. We explore the design choices of this class of algorithms using both theoretical analysis and empirical evaluation on downstream tasks. Our motivating examples are sparsity algorithms for language models, which often use top-$k$ to select the most important parameters or activations. We also release a fast bucketed top-$k$ implementation for PyTorch.