The ongoing trend of hardware specialization has led to a growing use of
custom data formats when processing sparse workloads, which are typically
memory-bound. These formats facilitate optimized software/hardware
implementations by utilizing sparsity pattern- or target-aware data structures
and layouts to enhance memory access latency and bandwidth utilization.
However, existing sparse tensor programming models and compilers offer little
or no support for productively customizing the sparse formats. Additionally,
because these frameworks represent formats using a limited set of per-dimension
attributes, they lack the flexibility to accommodate numerous new variations of
custom sparse data structures and layouts. To overcome this deficiency, we
propose UniSparse, an intermediate language that provides a unified abstraction
for representing and customizing sparse formats. Unlike the existing
attribute-based frameworks, UniSparse decouples the logical representation of
the sparse tensor (i.e., the data structure) from its low-level memory layout,
enabling the customization of both. As a result, a rich set of format
customizations can be succinctly expressed in a small set of well-defined
query, mutation, and layout primitives. We also develop a compiler leveraging
the MLIR infrastructure, which supports adaptive customization of formats, and
automatic code generation of format conversion and compute operations for
heterogeneous architectures. We demonstrate the efficacy of our approach
through experiments running commonly-used sparse linear algebra operations with
specialized formats on multiple different hardware targets, including an Intel
CPU, an NVIDIA GPU, an AMD Xilinx FPGA, and a simulated processing-in-memory
(PIM) device.

硬件专业化的趋势导致在处理稀疏工作负载时越来越多地使用自定义数据格式，而现有的稀疏张量编程模型和编译器对自定义格式的支持很少或没有。为了克服这个缺陷，我们提出了 UniSparse，它是一种提供统一抽象的中间语言，用于表示和自定义稀疏格式。与现有的基于属性的框架不同，UniSparse 将稀疏张量的逻辑表示（即数据结构）与其底层内存布局解耦，从而实现了两者的自定义。通过实验，我们证明了我们的方法在多种不同的硬件目标上运行常用的稀疏线性代数操作与专门格式的有效性，包括 Intel CPU、NVIDIA GPU、AMD Xilinx FPGA 和模拟的内存中处理（PIM）设备。