2,889 research outputs found
Towards a Software Transactional Memory for heterogeneous CPU-GPU processors
The heterogeneous Accelerated Processing Units (APUs) integrate a multi-core CPU and a GPU within the same chip. Modern APUs provide the programmer with platform atomics, used to communicate the CPU cores with the GPU using simple atomic datatypes. However, ensuring consistency for complex data types is a task delegated to programmers, who have to implement a mutual exclusion mechanism. Transactional Memory (TM) is an optimistic approach to implement mutual exclusion. With TM, shared data can be accessed by multiple computing threads speculatively, but changes are only visible if a transaction ends with no conflict with others in its memory accesses. TM has been studied and implemented in software and hardware for both CPU and GPU platforms, but an integrated solution has not been provided for APU processors.
In this paper we present APUTM, a software TM designed to work on heterogeneous APU processors. The design of APUTM focuses on minimizing the access to shared metadata in order to reduce the communication overhead via expensive platform atomics. The main objective of APUTM is to help us understand the tradeoffs of implementing a sofware TM on an heterogeneous CPU-GPU platform and to identify the key aspects to be considered in each device. In our experiments, we compare the adaptability of APUTM to execute in one of the devices (CPU or GPU) or in both of them simultaneously. These experiments show that APUTM is able to outperform sequential execution of the applications.This work has been supported by projects TIN2013-42253-P and TIN2016-80920-R, from the Spanish Government, P11-TIC8144 and P12- TIC1470, from Junta de AndalucĂa, and Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech
Hierarchical fractional-step approximations and parallel kinetic Monte Carlo algorithms
We present a mathematical framework for constructing and analyzing parallel
algorithms for lattice Kinetic Monte Carlo (KMC) simulations. The resulting
algorithms have the capacity to simulate a wide range of spatio-temporal scales
in spatially distributed, non-equilibrium physiochemical processes with complex
chemistry and transport micro-mechanisms. The algorithms can be tailored to
specific hierarchical parallel architectures such as multi-core processors or
clusters of Graphical Processing Units (GPUs). The proposed parallel algorithms
are controlled-error approximations of kinetic Monte Carlo algorithms,
departing from the predominant paradigm of creating parallel KMC algorithms
with exactly the same master equation as the serial one.
Our methodology relies on a spatial decomposition of the Markov operator
underlying the KMC algorithm into a hierarchy of operators corresponding to the
processors' structure in the parallel architecture. Based on this operator
decomposition, we formulate Fractional Step Approximation schemes by employing
the Trotter Theorem and its random variants; these schemes, (a) determine the
communication schedule} between processors, and (b) are run independently on
each processor through a serial KMC simulation, called a kernel, on each
fractional step time-window.
Furthermore, the proposed mathematical framework allows us to rigorously
justify the numerical and statistical consistency of the proposed algorithms,
showing the convergence of our approximating schemes to the original serial
KMC. The approach also provides a systematic evaluation of different processor
communicating schedules.Comment: 34 pages, 9 figure
Connected component identification and cluster update on GPU
Cluster identification tasks occur in a multitude of contexts in physics and
engineering such as, for instance, cluster algorithms for simulating spin
models, percolation simulations, segmentation problems in image processing, or
network analysis. While it has been shown that graphics processing units (GPUs)
can result in speedups of two to three orders of magnitude as compared to
serial codes on CPUs for the case of local and thus naturally parallelized
problems such as single-spin flip update simulations of spin models, the
situation is considerably more complicated for the non-local problem of cluster
or connected component identification. I discuss the suitability of different
approaches of parallelization of cluster labeling and cluster update algorithms
for calculations on GPU and compare to the performance of serial
implementations.Comment: 15 pages, 14 figures, one table, submitted to PR
- …