2,667 research outputs found
Scalable data abstractions for distributed parallel computations
The ability to express a program as a hierarchical composition of parts is an
essential tool in managing the complexity of software and a key abstraction
this provides is to separate the representation of data from the computation.
Many current parallel programming models use a shared memory model to provide
data abstraction but this doesn't scale well with large numbers of cores due to
non-determinism and access latency. This paper proposes a simple programming
model that allows scalable parallel programs to be expressed with distributed
representations of data and it provides the programmer with the flexibility to
employ shared or distributed styles of data-parallelism where applicable. It is
capable of an efficient implementation, and with the provision of a small set
of primitive capabilities in the hardware, it can be compiled to operate
directly on the hardware, in the same way stack-based allocation operates for
subroutines in sequential machines
High-performance and hardware-aware computing: proceedings of the second International Workshop on New Frontiers in High-performance and Hardware-aware Computing (HipHaC\u2711), San Antonio, Texas, USA, February 2011 ; (in conjunction with HPCA-17)
High-performance system architectures are increasingly exploiting heterogeneity. The HipHaC workshop aims at combining new aspects of parallel, heterogeneous, and reconfigurable microprocessor technologies with concepts of high-performance computing and, particularly, numerical solution methods. Compute- and memory-intensive applications can only benefit from the full
hardware potential if all features on all levels are taken into account in a holistic approach
Improving the scalability of parallel N-body applications with an event driven constraint based execution model
The scalability and efficiency of graph applications are significantly
constrained by conventional systems and their supporting programming models.
Technology trends like multicore, manycore, and heterogeneous system
architectures are introducing further challenges and possibilities for emerging
application domains such as graph applications. This paper explores the space
of effective parallel execution of ephemeral graphs that are dynamically
generated using the Barnes-Hut algorithm to exemplify dynamic workloads. The
workloads are expressed using the semantics of an Exascale computing execution
model called ParalleX. For comparison, results using conventional execution
model semantics are also presented. We find improved load balancing during
runtime and automatic parallelism discovery improving efficiency using the
advanced semantics for Exascale computing.Comment: 11 figure
A hybrid MPI-OpenMP scheme for scalable parallel pseudospectral computations for fluid turbulence
A hybrid scheme that utilizes MPI for distributed memory parallelism and
OpenMP for shared memory parallelism is presented. The work is motivated by the
desire to achieve exceptionally high Reynolds numbers in pseudospectral
computations of fluid turbulence on emerging petascale, high core-count,
massively parallel processing systems. The hybrid implementation derives from
and augments a well-tested scalable MPI-parallelized pseudospectral code. The
hybrid paradigm leads to a new picture for the domain decomposition of the
pseudospectral grids, which is helpful in understanding, among other things,
the 3D transpose of the global data that is necessary for the parallel fast
Fourier transforms that are the central component of the numerical
discretizations. Details of the hybrid implementation are provided, and
performance tests illustrate the utility of the method. It is shown that the
hybrid scheme achieves near ideal scalability up to ~20000 compute cores with a
maximum mean efficiency of 83%. Data are presented that demonstrate how to
choose the optimal number of MPI processes and OpenMP threads in order to
optimize code performance on two different platforms.Comment: Submitted to Parallel Computin
Resource-aware scheduling for 2D/3D multi-/many-core processor-memory systems
This dissertation addresses the complexities of 2D/3D multi-/many-core processor-memory systems, focusing on two key areas: enhancing timing predictability in real-time multi-core processors and optimizing performance within thermal constraints. The integration of an increasing number of transistors into compact chip designs, while boosting computational capacity, presents challenges in resource contention and thermal management. The first part of the thesis improves timing predictability. We enhance shared cache interference analysis for set-associative caches, advancing the calculation of Worst-Case Execution Time (WCET). This development enables accurate assessment of cache interference and the effectiveness of partitioned schedulers in real-world scenarios. We introduce TCPS, a novel task and cache-aware partitioned scheduler that optimizes cache partitioning based on task-specific WCET sensitivity, leading to improved schedulability and predictability. Our research explores various cache and scheduling configurations, providing insights into their performance trade-offs. The second part focuses on thermal management in 2D/3D many-core systems. Recognizing the limitations of Dynamic Voltage and Frequency Scaling (DVFS) in S-NUCA many-core processors, we propose synchronous thread migrations as a thermal management strategy. This approach culminates in the HotPotato scheduler, which balances performance and thermal safety. We also introduce 3D-TTP, a transient temperature-aware power budgeting strategy for 3D-stacked systems, reducing the need for Dynamic Thermal Management (DTM) activation. Finally, we present 3QUTM, a novel method for 3D-stacked systems that combines core DVFS and memory bank Low Power Modes with a learning algorithm, optimizing response times within thermal limits. This research contributes significantly to enhancing performance and thermal management in advanced processor-memory systems
A Parallel Mesh-Adaptive Framework for Hyperbolic Conservation Laws
We report on the development of a computational framework for the parallel,
mesh-adaptive solution of systems of hyperbolic conservation laws like the
time-dependent Euler equations in compressible gas dynamics or
Magneto-Hydrodynamics (MHD) and similar models in plasma physics. Local mesh
refinement is realized by the recursive bisection of grid blocks along each
spatial dimension, implemented numerical schemes include standard
finite-differences as well as shock-capturing central schemes, both in
connection with Runge-Kutta type integrators. Parallel execution is achieved
through a configurable hybrid of POSIX-multi-threading and MPI-distribution
with dynamic load balancing. One- two- and three-dimensional test computations
for the Euler equations have been carried out and show good parallel scaling
behavior. The Racoon framework is currently used to study the formation of
singularities in plasmas and fluids.Comment: late submissio
Data Provenance and Management in Radio Astronomy: A Stream Computing Approach
New approaches for data provenance and data management (DPDM) are required
for mega science projects like the Square Kilometer Array, characterized by
extremely large data volume and intense data rates, therefore demanding
innovative and highly efficient computational paradigms. In this context, we
explore a stream-computing approach with the emphasis on the use of
accelerators. In particular, we make use of a new generation of high
performance stream-based parallelization middleware known as InfoSphere
Streams. Its viability for managing and ensuring interoperability and integrity
of signal processing data pipelines is demonstrated in radio astronomy. IBM
InfoSphere Streams embraces the stream-computing paradigm. It is a shift from
conventional data mining techniques (involving analysis of existing data from
databases) towards real-time analytic processing. We discuss using InfoSphere
Streams for effective DPDM in radio astronomy and propose a way in which
InfoSphere Streams can be utilized for large antennae arrays. We present a
case-study: the InfoSphere Streams implementation of an autocorrelating
spectrometer, and using this example we discuss the advantages of the
stream-computing approach and the utilization of hardware accelerators
Building real-time embedded applications on QduinoMC: a web-connected 3D printer case study
Single Board Computers (SBCs) are now emerging
with multiple cores, ADCs, GPIOs, PWM channels, integrated
graphics, and several serial bus interfaces. The low power
consumption, small form factor and I/O interface capabilities of
SBCs with sensors and actuators makes them ideal in embedded
and real-time applications. However, most SBCs run non-realtime
operating systems based on Linux and Windows, and do
not provide a user-friendly API for application development. This
paper presents QduinoMC, a multicore extension to the popular
Arduino programming environment, which runs on the Quest
real-time operating system. QduinoMC is an extension of our earlier
single-core, real-time, multithreaded Qduino API. We show
the utility of QduinoMC by applying it to a specific application: a
web-connected 3D printer. This differs from existing 3D printers,
which run relatively simple firmware and lack operating system
support to spool multiple jobs, or interoperate with other devices
(e.g., in a print farm). We show how QduinoMC empowers devices with the capabilities to run new services without impacting their timing guarantees. While it is possible to modify existing operating systems to provide suitable timing guarantees, the effort to do so is cumbersome and does not provide the ease of programming afforded by QduinoMC.http://www.cs.bu.edu/fac/richwest/papers/rtas_2017.pdfAccepted manuscrip
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