Many-Task Computing and Blue Waters

This report discusses many-task computing (MTC) generically and in the context of the proposed Blue Waters systems, which is planned to be the largest NSF-funded supercomputer when it begins production use in 2012. The aim of this report is to inform the BW project about MTC, including understanding aspects of MTC applications that can be used to characterize the domain and understanding the implications of these aspects to middleware and policies. Many MTC applications do not neatly fit the stereotypes of high-performance computing (HPC) or high-throughput computing (HTC) applications. Like HTC applications, by definition MTC applications are structured as graphs of discrete tasks, with explicit input and output dependencies forming the graph edges. However, MTC applications have significant features that distinguish them from typical HTC applications. In particular, different engineering constraints for hardware and software must be met in order to support these applications. HTC applications have traditionally run on platforms such as grids and clusters, through either workflow systems or parallel programming systems. MTC applications, in contrast, will often demand a short time to solution, may be communication intensive or data intensive, and may comprise very short tasks. Therefore, hardware and software for MTC must be engineered to support the additional communication and I/O and must minimize task dispatch overheads. The hardware of large-scale HPC systems, with its high degree of parallelism and support for intensive communication, is well suited for MTC applications. However, HPC systems often lack a dynamic resource-provisioning feature, are not ideal for task communication via the file system, and have an I/O system that is not optimized for MTC-style applications. Hence, additional software support is likely to be required to gain full benefit from the HPC hardware

Doctor of Philosophy

dissertationRecent trends in high performance computing present larger and more diverse computers using multicore nodes possibly with accelerators and/or coprocessors and reduced memory. These changes pose formidable challenges for applications code to attain scalability. Software frameworks that execute machine-independent applications code using a runtime system that shields users from architectural complexities oer a portable solution for easy programming. The Uintah framework, for example, solves a broad class of large-scale problems on structured adaptive grids using fluid-flow solvers coupled with particle-based solids methods. However, the original Uintah code had limited scalability as tasks were run in a predefined order based solely on static analysis of the task graph and used only message passing interface (MPI) for parallelism. By using a new hybrid multithread and MPI runtime system, this research has made it possible for Uintah to scale to 700K central processing unit (CPU) cores when solving challenging fluid-structure interaction problems. Those problems often involve moving objects with adaptive mesh refinement and thus with highly variable and unpredictable work patterns. This research has also demonstrated an ability to run capability jobs on the heterogeneous systems with Nvidia graphics processing unit (GPU) accelerators or Intel Xeon Phi coprocessors. The new runtime system for Uintah executes directed acyclic graphs of computational tasks with a scalable asynchronous and dynamic runtime system for multicore CPUs and/or accelerators/coprocessors on a node. Uintah's clear separation between application and runtime code has led to scalability increases without significant changes to application code. This research concludes that the adaptive directed acyclic graph (DAG)-based approach provides a very powerful abstraction for solving challenging multiscale multiphysics engineering problems. Excellent scalability with regard to the different processors and communications performance are achieved on some of the largest and most powerful computers available today

NBODY6++GPU: Ready for the gravitational million-body problem

Accurate direct $N$-body simulations help to obtain detailed information about the dynamical evolution of star clusters. They also enable comparisons with analytical models and Fokker-Planck or Monte-Carlo methods. NBODY6 is a well-known direct $N$-body code for star clusters, and NBODY6++ is the extended version designed for large particle number simulations by supercomputers. We present NBODY6++GPU, an optimized version of NBODY6++ with hybrid parallelization methods (MPI, GPU, OpenMP, and AVX/SSE) to accelerate large direct $N$-body simulations, and in particular to solve the million-body problem. We discuss the new features of the NBODY6++GPU code, benchmarks, as well as the first results from a simulation of a realistic globular cluster initially containing a million particles. For million-body simulations, NBODY6++GPU is $400-2000$ times faster than NBODY6 with 320 CPU cores and 32 NVIDIA K20X GPUs. With this computing cluster specification, the simulations of million-body globular clusters including $5\%$ primordial binaries require about an hour per half-mass crossing time.Comment: 13 pages, 9 figures, 3 table

Investigating applications portability with the Uintah DAG-based runtime system on PetaScale supercomputers

pre-printPresent trends in high performance computing present formidable challenges for applications code using multicore nodes possibly with accelerators and/or co-processors and reduced memory while still attaining scalability. Software frameworks that execute machine-independent applications code using a runtime system that shields users from architectural complexities offer a possible solution. The Uintah framework for example, solves a broad class of large-scale problems on structured adaptive grids using fluid-flow solvers coupled with particle-based solids methods. Uintah executes directed acyclic graphs of computational tasks with a scalable asynchronous and dynamic runtime system for CPU cores and/or accelerators/coprocessors on a node. Uintah's clear separation between application and runtime code has led to scalability increases of 1000x without significant changes to application code. This methodology is tested on three leading Top500 machines; OLCF Titan, TACC Stampede and ALCF Mira using three diverse and challenging applications problems. This investigation of scalability with regard to the different processors and communications performance leads to the overall conclusion that the adaptive DAG-based approach provides a very powerful abstraction for solving challenging multi-scale multi-physics engineering problems on some of the largest and most powerful computers available today

Partial aggregation for collective communication in distributed memory machines

High Performance Computing (HPC) systems interconnect a large number of Processing Elements (PEs) in high-bandwidth networks to simulate complex scientific problems. The increasing scale of HPC systems poses great challenges on algorithm designers. As the average distance between PEs increases, data movement across hierarchical memory subsystems introduces high latency. Minimizing latency is particularly challenging in collective communications, where many PEs may interact in complex communication patterns. Although collective communications can be optimized for network-level parallelism, occasional synchronization delays due to dependencies in the communication pattern degrade application performance. To reduce the performance impact of communication and synchronization costs, parallel algorithms are designed with sophisticated latency hiding techniques. The principle is to interleave computation with asynchronous communication, which increases the overall occupancy of compute cores. However, collective communication primitives abstract parallelism which limits the integration of latency hiding techniques. Approaches to work around these limitations either modify the algorithmic structure of application codes, or replace collective primitives with verbose low-level communication calls. While these approaches give fine-grained control for latency hiding, implementing collective communication algorithms is challenging and requires expertise knowledge about HPC network topologies. A collective communication pattern is commonly described as a Directed Acyclic Graph (DAG) where a set of PEs, represented as vertices, resolve data dependencies through communication along the edges. Our approach improves latency hiding in collective communication through partial aggregation. Based on mathematical rules of binary operations and homomorphism, we expose data parallelism in a respective DAG to overlap computation with communication. The proposed concepts are implemented and evaluated with a subset of collective primitives in the Message Passing Interface (MPI), an established communication standard in scientific computing. An experimental analysis with communication-bound microbenchmarks shows considerable performance benefits for the evaluated collective primitives. A detailed case study with a large-scale distributed sort algorithm demonstrates, how partial aggregation significantly improves performance in data-intensive scenarios. Besides better latency hiding capabilities with collective communication primitives, our approach enables further optimizations of their implementations within MPI libraries. The vast amount of asynchronous programming models, which are actively studied in the HPC community, benefit from partial aggregation in collective communication patterns. Future work can utilize partial aggregation to improve the interaction of MPI collectives with acclerator architectures, and to design more efficient communication algorithms

Fibers are not (P)Threads: The Case for Loose Coupling of Asynchronous Programming Models and MPI Through Continuations

Asynchronous programming models (APM) are gaining more and more traction, allowing applications to expose the available concurrency to a runtime system tasked with coordinating the execution. While MPI has long provided support for multi-threaded communication and non-blocking operations, it falls short of adequately supporting APMs as correctly and efficiently handling MPI communication in different models is still a challenge. Meanwhile, new low-level implementations of light-weight, cooperatively scheduled execution contexts (fibers, aka user-level threads (ULT)) are meant to serve as a basis for higher-level APMs and their integration in MPI implementations has been proposed as a replacement for traditional POSIX thread support to alleviate these challenges. In this paper, we first establish a taxonomy in an attempt to clearly distinguish different concepts in the parallel software stack. We argue that the proposed tight integration of fiber implementations with MPI is neither warranted nor beneficial and instead is detrimental to the goal of MPI being a portable communication abstraction. We propose MPI Continuations as an extension to the MPI standard to provide callback-based notifications on completed operations, leading to a clear separation of concerns by providing a loose coupling mechanism between MPI and APMs. We show that this interface is flexible and interacts well with different APMs, namely OpenMP detached tasks, OmpSs-2, and Argobots.Comment: 12 pages, 7 figures Published in proceedings of EuroMPI/USA '20, September 21-24, 2020, Austin, TX, US