Topics for Student Theses
Table of contents
All topics in this list are available for Bachelor, Undergraduate, Diploma, and Master theses. We will discuss with you to scale the topic appropriately. Your own topic proposals are also very welcome.
High-performance Architectures
Systems in high-performance computing (HPC) need to cater to the demanding nature of applications in this field. Traditional requirements include low latency networking, cache-optimal memory placement, and timely coordination between compute nodes in the system. More recently, energy considerations have also become a concern. We study the implications these requirements and the specialised HPC hardware have for system-level software.
Scheduling
The scheduling of processes or threads in different environments (e.g. realtime, distributed, or high-performance systems) is governed by a diverse set of optimisation goals: throughput, timeliness, energy conservation, quality-of-service guarantees, to name but a few. Our work in this area tries to incorporate new approaches like machine learning or explores new areas like disaggregated systems.
The ATLAS scheduler1 uses metrics to predict task execution time. However, it is conceivable to use the same or a similar predictor to derive other application- and workload-related measures such as cache behavior, memory accesses, or energy consumption.
The thesis should experiment with the ATLAS predictor to analyse its ability and accuracy in predicting these other measures.
Supervisor: Michael Roitzsch, Till Smejkal, Jan Bierbaum
Fußnoten
Applications in high-performance computing (HPC) usually comprise many processes distributed across a cluster of compute nodes. From previous research and experience in Cloud computing, we learn that concurrently running multiple such applications can improve not only overall performance but also system utilisation and, in turn, energy consumption.
We want to employ gang-scheduling1 , a concept well-known in real-time systems, as a distributed system-level scheduler to ensure performance and efficient resource sharing at the same time. Gang scheduling caters to the close interactions among the processes comprising an application by running all of them at the same time.
For scaling to large clusters, gang scheduling needs to utilise the precise clocks available each compute node2: All clocks are synchronised, and the central gang scheduler broadcasts a list of application processes and time slots when they are to run. Node-local schedulers then enforce this schedule based on their local clocks.
Previous work on the node-local schedulers investigated different mechanisms for enacting a scheduling decision from user space. This approach is necessary for easy deployment as HPC clusters usually employ predefined system software. The goal of this task is to extend the existing work into a distributed gang scheduler by refining the node-local schedulers and adding a central coordinator.
Supervisor: Jan Bierbaum
Fußnoten
Applications in high-performance computing (HPC) usually comprise multiple processes that follow the bulk synchronous parallel (BSP)1 model: During its runtime, each process alternates between phases of independent, parallel computation and phases of communication and synchronisation. Due to the synchronisation phases, a single slow process (straggler) can delay the whole computation. So BSP simplifies programming at the expense of overall performance and system utilisation.
The ATLAS scheduler2 uses metrics to predict the execution time of applications. A metric is any value from the application domain that correlates with the amount of work to be performed. Using this predictor with an HPC application may allow to identify stragglers in advance. With this information we can either assign more (performant) compute resource to these slow processes or save energy by slowing down other processes.
In this thesis, the ATLAS predictor should be integrated with one or two simple HPC applications, e.g. from the NAS Parallel Benchmarks3. The student should identify suitable metrics within the application(s) and evaluate the prediction accuracy.
Supervisor: Jan Bierbaum
Footnotes
The ATLAS scheduler1 uses metrics to predict task execution time. So far, ATLAS has been applied to local scheduling of workloads on individual machines. However, it is conceivable to apply the same ideas of metric- and deadline-based scheduling to distributed scenarios.
The thesis should apply the ATLAS scheduling concepts to an application running distributed across multiple machines. Especially interesting are interactive cloud applications, where a tight end-to-end deadline is associated with each user-initiated request. Breaking down these end-to-end deadlines to individual scheduling obligations for the participating machines is one of the interesting challenges.
FractOS (formerly Caladan)2 is a research prototype for a distributed execution environment, which can be used as a substrate for the experiments.
Supervisor: Michael Roitzsch
References
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Michael Roitzsch, Stefan Wächtler, Hermann Härtig: ATLAS – Look-Ahead Scheduling Using Workload Metrics. RTAS 2013.
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Lluís Vilanova, Lina Maudlej, Matthias Hille, Nils Asmussen, Michael Roitzsch, Mark Silberstein: Caladan – A Distributed Meta-OS for Data Center Disaggregation. SPMA 2020.
One recent trend in data center architectures is the disaggregation of resources. Instead of having to equip each machine in a cluster with all types of devices, resource disaggregation aims at concentrating certain classes of devices in specialized nodes that do not execute applications themselves, but only serve as a broker for the resources they offer. Deploying a disaggregated architecture can increase resource utilization and the overall performance of a data center.
As with traditional architectures, isolating a high-priority task with tight latency requirements but low bandwidth utilization from other application's I/O noise is an interesting challenge in disagregated data centers. The goal of this thesis is to investigate how applications with real-time constraints for resource accesses are impacted by background applications in a disaggregated setup, particularly if the high-priority application and the background tasks use the same resource service. The analysis should be done on FractOS (formerly Caladan) [1], a research prototype for a disaggregated data center infrastructure.
Depending on the type of thesis, the applicability of work-constraining scheduling, as demonstrated with the K2 I/O scheduler [2], should be evaluated for a resource service in a disaggregated environment.
Supervisors: Till Miemietz Michael Roitzsch
References:
[1] Lluís Vilanova, Lina Maudlej, Matthias Hille, Michael Roitzsch, Mark Silberstein: Caladan - A Distributed Meta-OS for Data Center Disaggregation. SPMA 2020.
[2] Till Miemietz, Hannes Weisbach, Michael Roitzsch, Hermann Härtig: K2 - Work-Constraining Scheduling of NVMe-Attached Storage. RTSS 2019.
System Architecture
Trusted Computing
Virtualization
Miscellaneous
This category comprises topics that don't fit any of the other categories.
Code analysis tools simplify and facilitate understanding of the source code by programmers. For large projects such tools are indispensable. Unfortunately, the existing tools either do not provide a visual overview of the relevant code structure, lack interactivity for live analysis, or miss clarity by displaying too much information.
The goal of this project is to develop a tool that allows the programmer to introspect the code of a large repository. The key idea is to offer a programmer a flexible domain-specific language (DSL) for providing the requests to the source code. With such a language, the programmer can precisely specify what part of the source code must be shown and also how the resulting code shall be presented.
Allowing the visualisation to deviate from the actual code structure is of key importance because logical organisation sometimes deviates from the actual source code. Therefore, representing code literally may unnecessarily clutter the visualisation and complicate the code comprehension.
Short demo: Code analysis demo
Supervisor:Maksym Planeta