The Polyhedral Model Beyond Loops Recursion Optimization and Parallelization Through Polyhedral Modeling

International audienceThere may be a huge gap between the statements outlined by programmers in a program source code and instructions that are actually performed by a given processor architecture when running the executable code. This gap is due to the way the input code has been interpreted, translated and transformed by the compiler and the final processor hardware. Thus, there is an opportunity for efficient optimization strategies, that are dedicated to specific control structures and memory access patterns, to apply as soon as the actual runtime behavior has been discovered, even if they could not have been applied on the original source code. In this paper, we develop this idea by identifying code extracts that behave as polyhedral-compliant loops at runtime, while not having been outlined at all as loops in the original source code. In particular, we are interested in recursive functions whose runtime behavior can be modeled as polyhedral loops. Therefore, the scope of this study exclusively includes recursive functions whose control flow and memory accesses exhibit an affine behavior, which means that there exists a semantically equivalent affine loop nest, candidate for poly-hedral optimizations. Accordingly, our approach is based on analyzing early executions of a recursive program using a Nested Loop Recognition (NLR) algorithm, performing the affine loop modeling of the original program runtime behavior , which is then used to generate an equivalent iterative program, finally optimized using the polyhedral compiler Polly. We present some preliminary results showing that this approach brings recursion optimization techniques into a higher level in addition to widening the scope of the polyhe-dral model to include originally non-loop programs

Hyperscale Data Processing With Network-Centric Designs

Today’s largest data processing workloads are hosted in cloud data centers. Due to unprecedented data growth and the end of Moore’s Law, these workloads have ballooned to the hyperscale level, encompassing billions to trillions of data items and hundreds to thousands of machines per query. Enabling and expanding with these workloads are highly scalable data center networks that connect up to hundreds of thousands of networked servers. These massive scales fundamentally challenge the designs of both data processing systems and data center networks, and the classic layered designs are no longer sustainable. Rather than optimize these massive layers in silos, we build systems across them with principled network-centric designs. In current networks, we redesign data processing systems with network-awareness to minimize the cost of moving data in the network. In future networks, we propose new interfaces and services that the cloud infrastructure offers to applications and codesign data processing systems to achieve optimal query processing performance. To transform the network to future designs, we facilitate network innovation at scale. This dissertation presents a line of systems work that covers all three directions. It first discusses GraphRex, a network-aware system that combines classic database and systems techniques to push the performance of massive graph queries in current data centers. It then introduces data processing in disaggregated data centers, a promising new cloud proposal. It details TELEPORT, a compute pushdown feature that eliminates data processing performance bottlenecks in disaggregated data centers, and Redy, which provides high-performance caches using remote disaggregated memory. Finally, it presents MimicNet, a fine-grained simulation framework that evaluates network proposals at datacenter scale with machine learning approximation. These systems demonstrate that our ideas in network-centric designs achieve orders of magnitude higher efficiency compared to the state of the art at hyperscale

Ray: A Distributed Execution Engine for the Machine Learning Ecosystem

In recent years, growing data volumes and more sophisticated computational procedures have greatly increased the demand for computational power. Machine learning and artificial intelligence applications, for example, are notorious for their computational requirements. At the same time, Moores law is ending and processor speeds are stalling. As a result, distributed computing has become ubiquitous. While the cloud makes distributed hardware infrastructure widely accessible and therefore offers the potential of horizontal scale, developing these distributed algorithms and applications remains surprisingly hard. This is due to the inherent complexity of concurrent algorithms, the engineering challenges that arise when communicating between many machines, the requirements like fault tolerance and straggler mitigation that arise at large scale and the lack of a general-purpose distributed execution engine that can support a wide variety of applications.In this thesis, we study the requirements for a general-purpose distributed computation model and present a solution that is easy to use yet expressive and resilient to faults. At its core our model takes familiar concepts from serial programming, namely functions and classes, and generalizes them to the distributed world, therefore unifying stateless and stateful distributed computation. This model not only supports many machine learning workloads like training or serving, but is also a good t for cross-cutting machine learning applications like reinforcement learning and data processing applications like streaming or graph processing. We implement this computational model as an open-source system called Ray, which matches or exceeds the performance of specialized systems in many application domains, while also offering horizontally scalability and strong fault tolerance properties

Resource-aware Programming in a High-level Language - Improved performance with manageable effort on clustered MPSoCs

Bis 2001 bedeutete Moores und Dennards Gesetz eine Verdoppelung der Ausführungszeit alle 18 Monate durch verbesserte CPUs. Heute ist Nebenläufigkeit das dominante Mittel zur Beschleunigung von Supercomputern bis zu mobilen Geräten. Allerdings behindern neuere Phänomene wie "Dark Silicon" zunehmend eine weitere Beschleunigung durch Hardware. Um weitere Beschleunigung zu erreichen muss sich auch die Soft­ware mehr ihrer Hardware Resourcen gewahr werden. Verbunden mit diesem Phänomen ist eine immer heterogenere Hardware. Supercomputer integrieren Beschleuniger wie GPUs. Mobile SoCs (bspw. Smartphones) integrieren immer mehr Fähigkeiten. Spezialhardware auszunutzen ist eine bekannte Methode, um den Energieverbrauch zu senken, was ein weiterer wichtiger Aspekt ist, welcher mit der reinen Geschwindigkeit abgewogen werde muss. Zum Beispiel werden Supercomputer auch nach "Performance pro Watt" bewertet. Zur Zeit sind systemnahe low-level Programmierer es gewohnt über Hardware nachzudenken, während der gemeine high-level Programmierer es vorzieht von der Plattform möglichst zu abstrahieren (bspw. Cloud). "High-level" bedeutet nicht, dass Hardware irrelevant ist, sondern dass sie abstrahiert werden kann. Falls Sie eine Java-Anwendung für Android entwickeln, kann der Akku ein wichtiger Aspekt sein. Irgendwann müssen aber auch Hochsprachen resourcengewahr werden, um Geschwindigkeit oder Energieverbrauch zu verbessern. Innerhalb des Transregio "Invasive Computing" habe ich an diesen Problemen gearbeitet. In meiner Dissertation stelle ich ein Framework vor, mit dem man Hochsprachenanwendungen resourcengewahr machen kann, um so die Leistung zu verbessern. Das könnte beispielsweise erhöhte Effizienz oder schnellerer Ausführung für das System als Ganzes bringen. Ein Kerngedanke dabei ist, dass Anwendungen sich nicht selbst optimieren. Stattdessen geben sie alle Informationen an das Betriebssystem. Das Betriebssystem hat eine globale Sicht und trifft Entscheidungen über die Resourcen. Diesen Prozess nennen wir "Invasion". Die Aufgabe der Anwendung ist es, sich an diese Entscheidungen anzupassen, aber nicht selbst welche zu fällen. Die Herausforderung besteht darin eine Sprache zu definieren, mit der Anwendungen Resourcenbedingungen und Leistungsinformationen kommunizieren. So eine Sprache muss ausdrucksstark genug für komplexe Informationen, erweiterbar für neue Resourcentypen, und angenehm für den Programmierer sein. Die zentralen Beiträge dieser Dissertation sind: Ein theoretisches Modell der Resourcen-Verwaltung, um die Essenz des resourcengewahren Frameworks zu beschreiben, die Korrektheit der Entscheidungen des Betriebssystems bezüglich der Bedingungen einer Anwendung zu begründen und zum Beweis meiner Thesen von Effizienz und Beschleunigung in der Theorie. Ein Framework und eine Übersetzungspfad resourcengewahrer Programmierung für die Hochsprache X10. Zur Bewertung des Ansatzes haben wir Anwendungen aus dem High Performance Computing implementiert. Eine Beschleunigung von 5x konnte gemessen werden. Ein Speicherkonsistenzmodell für die X10 Programmiersprache, da dies ein notwendiger Schritt zu einer formalen Semantik ist, die das theoretische Modell und die konkrete Implementierung verknüpft. Zusammengefasst zeige ich, dass resourcengewahre Programmierung in Hoch\-sprachen auf zukünftigen Architekturen mit vielen Kernen mit vertretbarem Aufwand machbar ist und die Leistung verbessert

Design, Implementation and Evaluation of Parallel Solutions for a Nested Explainability Algorithm

In the field of Machine Learning and Data Science there is an escalating need for performance as workloads become more and more complex. Parallelization over multiple cores and machines (clusters) is often employed as a means to significantly improve performance. This work specifically considers the explainability algorithm GLEAMS (Global & Local ExplainAbility of black-box Models through Space partitioning) and the poor performance offered by its sequential Python implementation. GLEAMS is a post-hoc, model agnostic explainability technique capable of giving a global understanding of the original model through recursive partitioning of the input space into non overlapping cells, each featuring a local linear approximation of the black-box model. The purpose of this work is the analysis, development, implementation and testing of a parallel distributed solution for the sequential GLEAMS explainability algorithm. The algorithm poses certain interesting parallelization challenges such as a recursive binary tree and nested parallelism. Notably, the nested nature of the parallelism is of marked relevance due to the complexities it introduces and the poor support that existing Python frameworks and solutions offer for it. Multiple solutions were designed and implemented, and this paper describes the steps taken for their development, justifies the choices made, explains their workings, illustrates their differences and extensively analyses the performance offered. In particular, this work proposes an asyncio based approach, in combination with the Ray framework, as a practical solution to many of the limitations encountered with the current state of nested parallelism support in Python. Additionally, some theoretical and more general approaches and solutions inspired by other languages are proposed and discussed

Runtime-adaptive generalized task parallelism

Multi core systems are ubiquitous nowadays and their number is ever increasing. And while, limited by physical constraints, the computational power of the individual cores has been stagnating or even declining for years, a solution to effectively utilize the computational power that comes with the additional cores is yet to be found. Existing approaches to automatic parallelization are often highly specialized to exploit the parallelism of specific program patterns, and thus to parallelize a small subset of programs only. In addition, frequently used invasive runtime systems prohibit the combination of different approaches, which impedes the practicality of automatic parallelization. In the following thesis, we show that specializing to narrowly defined program patterns is not necessary to efficiently parallelize applications coming from different domains. We develop a generalizing approach to parallelization, which, driven by an underlying mathematical optimization problem, is able to make qualified parallelization decisions taking into account the involved runtime overhead. In combination with a specializing, adaptive runtime system the approach is able to match and even exceed the performance results achieved by specialized approaches.Mehrkernsysteme sind heutzutage allgegenwärtig und finden täglich weitere Verbreitung. Und während, limitiert durch die Grenzen des physikalisch Machbaren, die Rechenkraft der einzelnen Kerne bereits seit Jahren stagniert oder gar sinkt, existiert bis heute keine zufriedenstellende Lösung zur effektiven Ausnutzung der gebotenen Rechenkraft, die mit der steigenden Anzahl an Kernen einhergeht. Existierende Ansätze der automatischen Parallelisierung sind häufig hoch spezialisiert auf die Ausnutzung bestimmter Programm-Muster, und somit auf die Parallelisierung weniger Programmteile. Hinzu kommt, dass häufig verwendete invasive Laufzeitsysteme die Kombination mehrerer Parallelisierungs-Ansätze verhindern, was der Praxistauglichkeit und Reichweite automatischer Ansätze im Wege steht. In der Ihnen vorliegenden Arbeit zeigen wir, dass die Spezialisierung auf eng definierte Programmuster nicht notwendig ist, um Parallelität in Programmen verschiedener Domänen effizient auszunutzen. Wir entwickeln einen generalisierenden Ansatz der Parallelisierung, der, getrieben von einem mathematischen Optimierungsproblem, in der Lage ist, fundierte Parallelisierungsentscheidungen unter Berücksichtigung relevanter Kosten zu treffen. In Kombination mit einem spezialisierenden und adaptiven Laufzeitsystem ist der entwickelte Ansatz in der Lage, mit den Ergebnissen spezialisierter Ansätze mitzuhalten, oder diese gar zu übertreffen.Part of the work presented in this thesis was performed in the context of the SoftwareCluster project EMERGENT (http://www.software-cluster.org). It was funded by the German Federal Ministry of Education and Research (BMBF) under grant no. “01IC10S01”. Later work has been supported, also by the German Federal Ministry of Education and Research (BMBF), through funding for the Center for IT-Security, Privacy and Accountability (CISPA) under grant no. “16KIS0344”

10381 Summary and Abstracts Collection -- Robust Query Processing

Dagstuhl seminar 10381 on robust query processing (held 19.09.10 - 24.09.10) brought together a diverse set of researchers and practitioners with a broad range of expertise for the purpose of fostering discussion and collaboration regarding causes, opportunities, and solutions for achieving robust query processing. The seminar strove to build a unified view across the loosely-coupled system components responsible for the various stages of database query processing. Participants were chosen for their experience with database query processing and, where possible, their prior work in academic research or in product development towards robustness in database query processing. In order to pave the way to motivate, measure, and protect future advances in robust query processing, seminar 10381 focused on developing tests for measuring the robustness of query processing. In these proceedings, we first review the seminar topics, goals, and results, then present abstracts or notes of some of the seminar break-out sessions. We also include, as an appendix, the robust query processing reading list that was collected and distributed to participants before the seminar began, as well as summaries of a few of those papers that were contributed by some participants