Searcharxiv⌕ Search

arXiv subjects

Rongcui Dong

Publications and source records attributed to Rongcui Dong.

3 recordsLinked to original sources
arXiv2026

Symbolic Basic Block Profiling for Machine Learning Kernels

Current basic block profiling techniques obtain the count of executions of each basic block in a program using dynamic instrumentation. These profiling counters create runtime overheads and also require the execution of the program, which, for large input sizes, can take substantial time. We propose symbolic program profiling that generates symbolic formulae for a basic block's count with inputs as the independent variables. Our technique is limited in applicability to a certain class of programs, namely machine learning (ML) kernels. We implement our technique in the LLVM compiler and evaluate it on 78 ML operators from 50 different ML models. These operators are generated by TVM, a machine learning compiler. Our symbolic profiles deliver exactly the same results as dynamic instrumentation for 73 out of 78 kernels with a median speedup of 15093x.

cs.PL↗
arXiv2025

Modeling Utilization to Identify Shared-Memory Atomic Bottlenecks

Performance analysis is critical for GPU programs with data-dependent behavior, but models like Roofline are not very useful for them and interpreting raw performance counters is tedious. In this work, we present an analytical model for shared memory atomics (\emph{fetch-and-op} and \emph{compare-and-swap} instructions on NVIDIA Volta and Ampere GPU) that allows users to immediately determine if shared memory atomic operations are a bottleneck for a program's execution. Our model is based on modeling the architecture as a single-server queuing model whose inputs are performance counters. It captures load-dependent behavior such as pipelining, parallelism, and different access patterns. We embody this model in a tool that uses CUDA hardware counters as parameters to predict the utilization of the shared-memory atomic unit. To the best of our knowledge, no existing profiling tool or model provides this capability for shared-memory atomic operations. We used the model to compare two histogram kernels that use shared-memory atomics. Although nearly identical, their performance can be different by up to 30\%. Our tool correctly identifies a bottleneck shift from shared-memory atomic unit as the cause of this discrepancy.

cs.PF↗