Optimizing iterative data-flow scientific applications using directed cyclic graphs

Data-flow programming models have become a popular choice for writing parallel applications as an alternative to traditional work-sharing parallelism. They are better suited to write applications with irregular parallelism that can present load imbalance. However, these programming models suffer from overheads related to task creation, scheduling and dependency management, limiting performance and scalability when tasks become too small. At the same time, many HPC applications implement iterative methods or multi-step simulations that create the same directed acyclic graphs of tasks on each iteration. By giving application programmers a way to express that a specific loop is creating the same task pattern on each iteration, we can create a single task directed acyclic graph (DAG) once and transform it into a cyclic graph. This cyclic graph is then reused for successive iterations, minimizing task creation and dependency management overhead. This paper presents the taskiter, a new construct we propose for the OmpSs-2 and OpenMP programming models, allowing the use of directed cyclic task graphs (DCTG) to minimize runtime overheads. Moreover, we present a simple immediate successor locality-aware heuristic that minimizes task scheduling overhead by bypassing the runtime task scheduler. We evaluate the implementation of the taskiter and the immediate successor heuristic in 8 iterative benchmarks. Using small task granularities, we obtain a geometric mean speedup of 2.56x over the reference OmpSs-2 implementation, and a 3.77x and 5.2x speedup over the LLVM and GCC OpenMP runtimes, respectively.This work was supported in part by the European Union’s Horizon 2020/EuroHPC Research and Innovation Programme (DEEP-SEA) under Grant 955606; in part by the Spanish State Research Agency—Ministry of Science and Innovation, Generalitat de Catalunya, under Project PCI2021121958 and Project 2021-SGR-01007; in part by the Spanish Ministry of Science and Technology under Contract PID2019-107255GB; and in part by Severo Ochoa under Grant CEX2021-001148-S/MCIN/AEI/10.13039/501100011033.Peer ReviewedPostprint (published version

Enhancing Productivity and Performance Portability of General-Purpose Parallel Programming

This work focuses on compiler and run-time techniques for improving the productivity and the performance portability of general-purpose parallel programming. More specifically, we focus on shared-memory task-parallel languages, where the programmer explicitly exposes parallelism in the form of short tasks that may outnumber the cores by orders of magnitude. The compiler, the run-time, and the platform (henceforth the system) are responsible for harnessing this unpredictable amount of parallelism, which can vary from none to excessive, towards efficient execution. The challenge arises from the aspiration to support fine-grained irregular computations and nested parallelism. This work is even more ambitious by also aspiring to lay the foundations to efficiently support declarative code, where the programmer exposes all available parallelism, using high-level language constructs such as parallel loops, reducers or futures. The appeal of declarative code is twofold for general-purpose programming: it is often easier for the programmer who does not have to worry about the granularity of the exposed parallelism, and it achieves better performance portability by avoiding overfitting to a small range of platforms and inputs for which the programmer is coarsening. Furthermore, PRAM algorithms, an important class of parallel algorithms, naturally lend themselves to declarative programming, so supporting it is a necessary condition for capitalizing on the wealth of the PRAM theory. Unfortunately, declarative codes often expose such an overwhelming number of fine-grained tasks that existing systems fail to deliver performance. Our contributions can be partitioned into three components. First, we tackle the issue of coarsening, which declarative code leaves to the system. We identify two goals of coarsening and advocate tackling them separately, using static compiler transformations for one and dynamic run-time approaches for the other. Additionally, we present evidence that the current practice of burdening the programmer with coarsening either leads to codes with poor performance-portability, or to a significantly increased programming effort. This is a ``show-stopper'' for general-purpose programming. To compare the performance portability among approaches, we define an experimental framework and two metrics, and we demonstrate that our approaches are preferable. We close the chapter on coarsening by presenting compiler transformations that automatically coarsen some types of very fine-grained codes. Second, we propose Lazy Scheduling, an innovative run-time scheduling technique that infers the platform load at run-time, using information already maintained. Based on the inferred load, Lazy Scheduling adapts the amount of available parallelism it exposes for parallel execution and, thus, saves parallelism overheads that existing approaches pay. We implement Lazy Scheduling and present experimental results on four different platforms. The results show that Lazy Scheduling is vastly superior for declarative codes and competitive, if not better, for coarsened codes. Moreover, Lazy Scheduling is also superior in terms of performance-portability, supporting our thesis that it is possible to achieve reasonable efficiency and performance portability with declarative codes. Finally, we also implement Lazy Scheduling on XMT, an experimental manycore platform developed at the University of Maryland, which was designed to support codes derived from PRAM algorithms. On XMT, we manage to harness the existing hardware support for scheduling flat parallelism to compose it with Lazy Scheduling, which supports nested parallelism. In the resulting hybrid scheduler, the hardware and software work in synergy to overcome each other's weaknesses. We show the performance composability of the hardware and software schedulers, both in an abstract cost model and experimentally, as the hybrid always performs better than the software scheduler alone. Furthermore, the cost model is validated by using it to predict if it is preferable to execute a code sequentially, with outer parallelism, or with nested parallelism, depending on the input, the available hardware parallelism and the calling context of the parallel code

Coordinating Resource Use in Open Distributed Systems

In an open distributed system, computational resources are peer-owned, and distributed over time and space. The system is open to interactions with its environment, and the resources can dynamically join or leave the system, or can be discovered at runtime. This dynamicity leads to opportunities to carry out computations without statically owned resources, harnessing the collective compute power of the resources connected by the Internet. However, realizing this potential requires efficient and scalable resource discovery, coordination, and control, which present challenges in a dynamic, open environment. In this thesis, I present an approach to address these challenges by separating the functionality concerns of concurrent computations from those of coordinating their resource use, with the purpose of reducing programming complexity, and aiding development of correct, efficient, and resource-aware concurrent programs. As a first step towards effectively coordinating distributed resources, I developed DREAM, a Distributed Resource Estimation and Allocation Model, which enables computations to reason about future availability of resources. I then developed a fine-grained resource coordination scheme for distributed computations. The coordination scheme integrates DREAM-based resource reasoning into a distributed scheduler, for deciding and enforcing fine-grained resource-use schedules for distributed computations. To control the overhead caused by the coordination, a tuner is implemented which explicitly balances the overhead of the control mechanisms against the extent of control exercised. The effectiveness and performance of the resource coordination approach have been evaluated using a number of case studies. Experimental results show that the approach can effectively schedule computations for supporting various types of coordination objectives, such as ensuring Quality-of-Service, power-efficient execution, and dynamic load balancing. The overhead caused by the coordination mechanism is relatively modest, and adjustable through the tuner. In addition, the coordination mechanism does not add extra programming complexity to computations

Group Scheduling in SELinux to Mitigate CPU-Focused Denial of Service Attacks

Popular security techniques such as public-private key encryption, firewalls, and role-based access control offer significant protec-tion of system data, but offer only limited protection of the computations using that data from significant interference due to accident or adversarial attack. However, in an increasing number of modern systems, ensuring the reliable execution of system activities is every bit as important as ensuring data security. This paper makes three contributions to the state of the art in protection of the execution of system activities from accidental or adversarial interference. First, we consider the motivating problem of CPU-focused denial of service attacks, and explain how limitations of current approaches to these kinds of attacks make it difficult to offer sufficiently rigorous and fine-grained assurances of protection for the execution of system computations. Second, we describe a novel solution approach in which we have integrated fine-grained scheduling decision functions with system call hooks from the Security Enhanced Linux (SELinux) framework within the Linux 2.6 kernel. Third, we present empirical evaluations of the efficacy of our approach in controlling the CPU utilization of competing greedy computations that are either completely CPU bound, or that interleave I/O and CPU access, across a range of relative allocations of the CPU

The Beginnings and Prospective Ending of “End-to-End”: An Evolutionary Perspective On the Internet’s Architecture

The technology of “the Internet” is not static. Although its “end-to- end” architecture has made this “connection-less” communications system readily “extensible,” and highly encouraging to innovation both in hardware and software applications, there are strong pressures for engineering changes. Some of these are wanted to support novel transport services (e.g. voice telephony, real-time video); others would address drawbacks that appeared with opening of the Internet to public and commercial traffic - e.g., the difficulties of blocking delivery of offensive content, suppressing malicious actions (e.g. “denial of service” attacks), pricing bandwidth usage to reduce congestion. The expected gains from making “improvements” in the core of the network should be weighed against the loss of the social and economic benefits that derive from the “end-to-end” architectural design. Even where technological “fixes” can be placed at the networks’ edges, the option remains to search for alternative, institutional mechanisms of governing conduct in cyberspace.

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016)

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.The PhD Symposium was a very good opportunity for the young researchers to share information and knowledge, to present their current research, and to discuss topics with other students in order to look for synergies and common research topics. The idea was very successful and the assessment made by the PhD Student was very good. It also helped to achieve one of the major goals of the NESUS Action: to establish an open European research network targeting sustainable solutions for ultrascale computing aiming at cross fertilization among HPC, large scale distributed systems, and big data management, training, contributing to glue disparate researchers working across different areas and provide a meeting ground for researchers in these separate areas to exchange ideas, to identify synergies, and to pursue common activities in research topics such as sustainable software solutions (applications and system software stack), data management, energy efficiency, and resilience.European Cooperation in Science and Technology. COS

The Aaron Diamond Foundation AIDS Research in New York City

Case study examines a foundation that spent down its $200 million endowment over 10 years on programs in medical research, minority education, and culture in New York City

The Cilk system for parallel multithreaded computing

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.Includes bibliographical references (p. 187-199).by Christopher F. Joerg.Ph.D