# Feedbacks on SparseTIR project

## Table of Contents

## DONE Talk @ Google MLIR Team

A collection of useful questions:

- TACO can also decompose computations and schedule each of them, so what's the advantage of SparseTIR?
- Do SparseTIR support reorder dimensions (such as [1, 0] in MLIR Sparse Dialect)?
- CPU results, and 2:4 sparse tensor core support.
- Preprocessing overhead, how good is SparseTIR in the setting that sparse pattern is dynamic.

## DONE Qualifying Project Presentation @ UW

Feedbacks from TQ:

Some notes on your talk (for future refs):

- Three stage compilation can be a bit too detailed, would be useful to have a "map"(outline) that you get back to
- Think about high-level punchlines (composability) and come back to it in your talk
- Try to increase your voice on punchlines(here is the key take-away! …) this helps to make your talk more animated.
- Good job bringing examples on transformations
- Avoid discussing technical details in exp and focus on result, show results, then mention what you did briefly
- Come back to your key takeaway (composability) in exps
At a high level: Think about your overall flow and try to always get audiences back to it Try to be animated and emphasize on punch-lines QA:

- try to repeat the question from audience(so you can answer, wrt to your interpretation, instead of try to guess what others ask)

Other questions:

- relations with Taichi-like frameworks.

## DONE Talk @ Cornell Zhang Group

Feedbacks:

- Should explain more about the runtime/compile-time behavior about format decomposition.
- Both the presentation and paper is confusing about that.

- Release code soon so that other research groups could benefit from that.

### DONE Send a followup email

## DONE Sync with SparseLNR folks

Digest:

- SparseLNR extends TACO to support
`rfactor`

and`compute_at`

primitives. - The original idea of SparseLNR comes from how to describe FusedMM in TACO.
- SparseLNR do not support vectorized intrinsics while SparseTIR can help support it.

- It's better to write some interactive demos about SparseTIR transformations.

## DONE Talk @ Tsinghua

Digest:

- Look at segment group and atomic parallelism.
- Investigate the overhead of runtime load-balancing compared to compiled-time load-balancing.
- More clarification on inputs/outputs/format decomposition.
- Future work: summarize some common sparse patterns.

### TODO Sync with Genghan

## DONE ASPLOS Rebuttal

Digest:

- Experiments on DNN sparsity (sparse conv, etc.).
- Performance breakdown analysis.

## DONE Talk @ Amazon AI

Digest:

- Questions from Minjie: tuning overhead.

## TODO Feedbacks from Yinuo

They want HPC operators:

GAP

- Breadth-First Search
- PageRank
- Single-Source Shortest Path
- Connected Components
- Betweenness Centrality
- Triangle Counting

HPCC

- Gauss-Jordan Elimination
- RandomAccess
- Stentil

HPCG

- Spars Matrix-Vector Multiplication
- Sparse Matrix-Matrix Multiplication
- Sampled Dense Matrix Multiplication
- Sparse Triangular Solve
- Symmetric Gauss-Seidel Smoother

NAS

- Conjugate Gradient
- Integer Sort

TPC -Hash-Join

Should take some time to investigate them.