This is a corrigendum for the article "Falcon: A Scalable Analytical Cache Model" published in Proc. ACM Program. Lang. 8, PLDI, Article 222 (Jun 2024). We make corrections to the experimental evaluation and provide updated data.

Compilers often use performance models to decide how to optimize code. This is often preferred over using hardware performance measurements, since hardware measurements can be expensive, limited by hardware availability, and makes the output of compilation non-deterministic. Analytical models, on the other hand, serve as efficient and noise-free performance indicators. Since many optimizations focus on improving memory performance, memory cache miss rate estimations can serve as an effective and noise-free performance indicator for superoptimizers, worst-case execution time analyses, manual program optimization, and many other performance-focused use cases. Existing methods to model the cache behavior of affine programs work on small programs such as those in the Polybench benchmark but do not scale to the larger programs we would like to optimize in production, which can be orders of magnitude bigger by lines of code. These analytical approaches hand off the whole program to a Presburger solver and perform expensive mathematical operations on the huge resulting formulas. We develop a scalable cache model for affine programs that splits the computation into smaller pieces that do not trigger the worst-case asymptotic behavior of these solvers. We evaluate our approach on 46 TorchVision neural networks, finding that our model has a geomean runtime of 44.9 seconds compared to over 32 minutes for the state-of-the-art prior cache model, and the latter is actually smaller than the true value because the prior model reached our four-hour time limit on 54% of the networks, and this limit was never reached by our tool. Our model exploits parallelism effectively: running it on sixteen cores is 8.2x faster than running it single-threaded. While the state-of-the-art model takes over four hours to analyze a majority of the benchmark programs, Falcon produces results in at most 3 minutes and 3 seconds; moreover, after a local modification to the program being analyzed, our model efficiently updates the predictions in 513 ms on average (geomean). Thus, we provide the first scalable analytical cache model. CCS Concepts: • Software and its engineering → Compilers .

This paper proposes a scalable distributed video analytics framework that can process thousands of video streams from sources such as CCTV cameras using semantic scene analysis. The main idea is to deploy deep learning pipelines on the fog nodes and generate semantic scene description records (SDRs) of video feeds from the associated CCTV cameras. These SDRs are transmitted to the cloud instead of video frames saving on network bandwidth. Using these SDRs stored on the cloud database, we can answer many complex queries and perform rich video analytics, within extremely low latencies. There is no need to scan and process the video streams again on a per query basis. The software architecture on the fog nodes allows for integrating new deep learning pipelines dynamically into the existing system, thereby supporting novel analytics and queries. We demonstrate the effectiveness of the system by proposing a novel distributed algorithm for real-time vehicle pursuit. The proposed algorithm involves asking multiple spatio-temporal queries in an adaptive fashion to reduce the query processing time and is robust to inaccuracies in the deployed deep learning pipelines and camera failures.