For many, Graphics Processing Units (GPUs) provides a source of reliable computing power. Recently, Nvidia introduced its 9th generation HPC-grade GPUs, the Ampere 100, claiming significant performance improvements over previous generations, particularly for AI-workloads, as well as introducing new architectural features such as asynchronous data movement. But how well does the A100 perform on non-AI benchmarks, and can we expect the A100 to deliver the application improvements we have grown used to with previous GPU generations? In this paper, we benchmark the A100 GPU and compare it to four previous generations of GPUs, with particular focus on empirically quantifying our derived performance expectations, and -- should those expectations be undelivered -- investigate whether the introduced data-movement features can offset any eventual loss in performance? We find that the A100 delivers less performance increase than previous generations for the well-known Rodinia benchmark suite; we show that some of these performance anomalies can be remedied through clever use of the new data-movement features, which we microbenchmark and demonstrate where (and more importantly, how) they should be used.

中文翻译：

---

对 Nvidia GPU 谱系进行基准测试

对于许多人来说，图形处理单元 (GPU) 提供了可靠的计算能力来源。最近，英伟达推出了其第 9 代 HPC 级 GPU，即 Ampere 100，声称与前几代相比性能有了显着提升，尤其是在 AI 工作负载方面，并引入了异步数据移动等新架构特性。但是 A100 在非 AI 基准测试中的表现如何，我们能否期望 A100 提供我们已经习惯于使用前几代 GPU 的应用程序改进？在本文中，我们对 A100 GPU 进行了基准测试，并将其与前四代 GPU 进行了比较，特别关注经验量化我们得出的性能预期，并且——如果这些期望没有实现——调查引入的数据移动功能是否可以抵消任何最终的性能损失？我们发现，对于著名的 Rodinia 基准测试套件，A100 的性能提升幅度低于前几代；我们展示了其中一些性能异常可以通过巧妙使用新的数据移动功能来补救，我们对这些功能进行了微基准测试并演示了它们应该在哪里（更重要的是，如何）使用。