一句话：提出一种编译技术，将动态稀疏张量代数运算编译为适配多种指针式动态稀疏格式的高效代码。
许多应用需要计算随程序执行而演化的稀疏数据，这类数据可表示为动态稀疏张量，并适合用块链表、二叉搜索树、B树、C树等基于指针的动态格式存储，以支持快速原地修改。然而，不同动态格式各有优劣，且针对不同格式实现张量运算的代码差异大，正确实现与优化都很困难。本文提出一种用于精确指定递归、指针式数据结构的语言，能够表达上述多种动态数据结构；进而展示编译器如何根据这些高层数据结构规格，生成高效访问和计算动态稀疏张量的代码。评估表明，该技术生成的动态稀疏张量代数内核性能与 PAM、Aspen、STINGER、Terrace 等先进库和框架相当甚至更好，同时支持比 Aspen、STINGER、Terrace 更广的张量代数操作，例如同时计算静态与动态稀疏张量，并在相同操作上显著优于 PAM。
Many applications, from social network graph analytics to control flow analysis, compute on sparse data that evolves over the course of program execution. Such data can be represented as dynamic sparse tensors and efficiently stored in formats (data layouts) that utilize pointer-based data structures like block linked lists, binary search trees, B-trees, and C-trees among others. These specialized formats support fast in-place modification and are thus better suited than traditional, array-based data structures like CSR for storing dynamic sparse tensors. However, different dynamic sparse tensor formats have distinct benefits and drawbacks, and performing different computations on tensors that are stored in different formats can require vastly dissimilar code that are not straightforward to correctly implement and optimize. This paper shows how a compiler can generate efficient code to compute tensor algebra operations on dynamic sparse tensors that may be stored in a wide range of disparate formats. We propose a language for precisely specifying recursive, pointer-based data structures, and we show how this language can express many different dynamic data structures, including all the ones named above as well as many more. We then describe how, given high-level specifications of such dynamic data structures, a compiler can emit code to efficiently access and compute on dynamic sparse tensors that are stored in the aforementioned data structures. We evaluate our technique and find it generates efficient dynamic sparse tensor algebra kernels that have performance comparable to, if not better than, state-of-the-art libraries and frameworks such as PAM, Aspen, STINGER, and Terrace. At the same time, our technique supports a wider range of tensor algebra operations---such as those that simultaneously compute with static and dynamic sparse tensors---than Aspen, STINGER, and Terrace, while also achieving significantly better performance than PAM for those same operations.