Data Management and Visualization for Benchmarking Deep Learning Training Systems

Ties Robroek, Aaron Duane, Ehsan Yousefzadeh-Asl-Miandoab, Pınar Tözün

Accepted to DEEM 2023 (Best Presentation Award)



Evaluating hardware for deep learning is challenging. The models can take days or more to run, the datasets are generally larger than what fits into memory, and the models are sensitive to interference. Scaling this up to a large amount of experiments and keeping track of both software and hardware metrics thus poses real difficulties as these problems are exacerbated by sheer experimental data volume. This paper explores some of the data management and exploration difficulties when working on machine learning systems research. We introduce our solution in the form of an open-source framework built on top of a machine learning lifecycle platform. Additionally, we introduce a web environment for visualizing and exploring experimental data.

Profiling and Monitoring Deep Learning Training Tasks

Ehsan Yousefzadeh-Asl-Miandoab, Ties Robroek, Pınar Tözün

Accepted to EuroMLSys 2023


The embarrassingly parallel nature of deep learning training tasks makes CPU-GPU co-processors the primary commodity hardware for them. The computing and memory requirements of these tasks, however, do not always align well with the available GPU resources. It is, therefore, important to monitor and profile the behavior of training tasks on co-processors to understand better the requirements of different use cases. In this paper, our goal is to shed more light on the variety of tools for profiling and monitoring deep learning training tasks on server-grade NVIDIA GPUs. In addition to surveying the main characteristics of the tools, we analyze the functional limitations and overheads of each tool by using a both light and heavy training scenario. Our results show that monitoring tools like nvidia-smi and dcgm can be integrated with resource managers for online decision making thanks to their low overheads. On the other hand, one has to be careful about the set of metrics to correctly reason about the GPU utilization. When it comes to profiling, each tool has its time to shine; a framework-based or system-wide GPU profiler can first detect the frequent kernels or bottlenecks, and then, a lower-level GPU profiler can focus on particular kernels at the micro-architectural-level.

TPCx-AI on NVIDIA Jetsons

Robert Bayer, Jon Voigt Tøttrup, Pınar Tözün

Accepted to TPCTC 2022


Despite their resource- power-constrained nature, edge devices also exhibit an increase in the available compute and memory resources and heterogeneity, similar to the evolution of server hardware in the past decade. For example, NVIDIA Jetson devices have a system-on-chip (SoC) composed of an ARM CPU and an NVIDIA GPU sharing RAM that could be up to 32 GB. Such an SoC setup offers opportunities to push down complex computations closer to the data source rather than performing them on remote servers.
In this paper, we characterize the performance of two types of NVIDIA Jetson devices for end-to-end machine learning pipelines using the TPCx-AI benchmark. Our results demonstrate that the available memory is the main limitation to performance and scaling up machine learning workloads on edge devices. Despite this limitation, some edge devices show promise when comparing against a desktop hardware in terms of power-efficiency and reduction in data movement. In addition, exploiting the available compute parallelism on these devices can benefit not just model training and inference but also data pre-processing. By parallelizing, we get close to an order of magnitude improvement in pre-processing time for one of the TPCx-AI use cases. Finally, while TPCx-AI is a valuable benchmark, it is designed for server settings; therefore, the community needs an end-to-end machine learning benchmark targeting IoT/edge.

Micro-architectural Analysis of a Learned Index

Mikkel Møller Andersen, Pınar Tözün

Published in aiDM 2022


Since the publication of The Case for Learned Index Structures in 2018, there has been a rise in research that focuses on learned indexes for different domains and with different functionalities. While the effectiveness of learned indexes as an alternative to traditional index structures such as B+Trees have already been demonstrated by several studies, previous work tend to focus on higher-level performance metrics such as throughput and index size. In this paper, our goal is to dig deeper and investigate how learned indexes behave at a micro-architectural level.
More specifically, we focus on previously proposed learned index structure ALEX, which is a tree-based in-memory index structur that consists of a hierarchy of machine learned models. Unlike the original proposal for learned indexes, ALEX is designed from the ground up to allow updates and inserts. Therefore, it enables more dynamic workloads using learned indexes. In this work, we perform a micro-architectural analysis of ALEX and compare its behavior to the tree-based index structures that are not based on learned models, i.e., ART and B+Tree.
Our results show that ALEX is bound by memory stalls, mainly stalls due to data misses from the last-level cache. Compared to ART and B+Tree, ALEX exhibits fewer stalls and a lower cycles-perinstruction value across different workloads. On the other hand, the amount of instructions required to handle out-of-bound inserts in ALEX can increase the instructions needed per request significantly (10X) for write-heavy workloads. However, the micro-architectural behavior shows that this increase in the instruction footprint exhibit high instruction-level parallelism, and, therefore, does not negatively impact the overall execution time.

Training for Speech Recognition on Co-processors

Sebastian Baunsgaard, Sebastian Benjamin Wrede, Pınar Tözün

Published in ADMS 2020


Automatic Speech Recognition (ASR) has increased in popularity in recent years. The evolution of processor and storage technologies has enabled more advanced ASR mechanisms, fueling the development of virtual assistants such as Amazon Alexa, Apple Siri, Microsoft Cortana, and Google Home. The interest in such assistants, in turn, has amplified the novel developments in ASR research. However, despite this popularity, there has not been a detailed training efficiency analysis of modern ASR systems. This mainly stems from: the proprietary nature of many modern applications that depend on ASR; the relatively expensive co-processor hardware that is used to accelerate ASR by big vendors to enable such applications; and the absence of well-established benchmarks. The goal of this paper is to address the latter two of these challenges.
The paper first describes an ASR model, based on a deep neural network inspired by recent work, and our experiences building it. Then we evaluate this model on three CPU-GPU co-processor platforms that represent different budget categories. Our results demonstrate that utilizing hardware acceleration yields good results even without high-end equipment. While the most expensive platform (10X price of the least expensive one) converges to the initial accuracy target 10-30% and 60-70% faster than the other two, the differences among the platforms almost disappear at slightly higher accuracy targets. In addition, our results further highlight both the difficulty of evaluating ASR systems due to the complex, long, and resource-intensive nature of the model training in this domain, and the importance of establishing benchmarks for ASR.