3,329 research outputs found
When parallel speedups hit the memory wall
After Amdahl's trailblazing work, many other authors proposed analytical
speedup models but none have considered the limiting effect of the memory wall.
These models exploited aspects such as problem-size variation, memory size,
communication overhead, and synchronization overhead, but data-access delays
are assumed to be constant. Nevertheless, such delays can vary, for example,
according to the number of cores used and the ratio between processor and
memory frequencies. Given the large number of possible configurations of
operating frequency and number of cores that current architectures can offer,
suitable speedup models to describe such variations among these configurations
are quite desirable for off-line or on-line scheduling decisions. This work
proposes new parallel speedup models that account for variations of the average
data-access delay to describe the limiting effect of the memory wall on
parallel speedups. Analytical results indicate that the proposed modeling can
capture the desired behavior while experimental hardware results validate the
former. Additionally, we show that when accounting for parameters that reflect
the intrinsic characteristics of the applications, such as degree of
parallelism and susceptibility to the memory wall, our proposal has significant
advantages over machine-learning-based modeling. Moreover, besides being
black-box modeling, our experiments show that conventional machine-learning
modeling needs about one order of magnitude more measurements to reach the same
level of accuracy achieved in our modeling.Comment: 24 page
Examining subgrid models of supermassive black holes in cosmological simulation
While supermassive black holes (SMBHs) play an important role in galaxy and
cluster evolution, at present they can only be included in large-scale
cosmological simulation via subgrid techniques. However, these subgrid models
have not been studied in a systematic fashion. Using a newly-developed fast,
parallel spherical overdensity halo finder built into the simulation code
FLASH, we perform a suite of dark matter-only cosmological simulations to study
the effects of subgrid model choice on relations between SMBH mass and dark
matter halo mass and velocity dispersion. We examine three aspects of SMBH
subgrid models: the choice of initial black hole seed mass, the test for
merging two black holes, and the frequency of applying the subgrid model. We
also examine the role that merging can play in determining the relations,
ignoring the complicating effects of SMBH-driven accretion and feedback. We
find that the choice of subgrid model can dramatically affect the black hole
merger rate, the cosmic SMBH mass density, and the low-redshift relations to
halo properties. We also find that it is possible to reproduce observations of
the low-redshift relations without accretion and feedback, depending on the
choice of subgrid model.Comment: 12 pages, 12 figures, revised from referee comments, accepted by Ap
Thread-Modular Static Analysis for Relaxed Memory Models
We propose a memory-model-aware static program analysis method for accurately
analyzing the behavior of concurrent software running on processors with weak
consistency models such as x86-TSO, SPARC-PSO, and SPARC-RMO. At the center of
our method is a unified framework for deciding the feasibility of inter-thread
interferences to avoid propagating spurious data flows during static analysis
and thus boost the performance of the static analyzer. We formulate the
checking of interference feasibility as a set of Datalog rules which are both
efficiently solvable and general enough to capture a range of hardware-level
memory models. Compared to existing techniques, our method can significantly
reduce the number of bogus alarms as well as unsound proofs. We implemented the
method and evaluated it on a large set of multithreaded C programs. Our
experiments showthe method significantly outperforms state-of-the-art
techniques in terms of accuracy with only moderate run-time overhead.Comment: revised version of the ESEC/FSE 2017 pape
Dynamic Power Management for Neuromorphic Many-Core Systems
This work presents a dynamic power management architecture for neuromorphic
many core systems such as SpiNNaker. A fast dynamic voltage and frequency
scaling (DVFS) technique is presented which allows the processing elements (PE)
to change their supply voltage and clock frequency individually and
autonomously within less than 100 ns. This is employed by the neuromorphic
simulation software flow, which defines the performance level (PL) of the PE
based on the actual workload within each simulation cycle. A test chip in 28 nm
SLP CMOS technology has been implemented. It includes 4 PEs which can be scaled
from 0.7 V to 1.0 V with frequencies from 125 MHz to 500 MHz at three distinct
PLs. By measurement of three neuromorphic benchmarks it is shown that the total
PE power consumption can be reduced by 75%, with 80% baseline power reduction
and a 50% reduction of energy per neuron and synapse computation, all while
maintaining temporary peak system performance to achieve biological real-time
operation of the system. A numerical model of this power management model is
derived which allows DVFS architecture exploration for neuromorphics. The
proposed technique is to be used for the second generation SpiNNaker
neuromorphic many core system
Implementation of digital pheromones in PSO accelerated by commodity Graphics Hardware
In this paper, a model for Graphics Processing Unit (GPU) implementation of Particle Swarm Optimization (PSO) using digital pheromones to coordinate swarms within ndimensional design spaces is presented. Previous work by the authors demonstrated the capability of digital pheromones within PSO for searching n-dimensional design spaces with improved accuracy, efficiency and reliability in both serial and parallel computing environments using traditional CPUs. Modern GPUs have proven to outperform the number of floating point operations when compared to CPUs through inherent data parallel architecture and higher bandwidth capabilities. The advent of programmable graphics hardware in the recent times further provided a suitable platform for scientific computing particularly in the field of design optimization. However, the data parallel architecture of GPUs requires a specialized formulation for leveraging its computational capabilities. When the objective function computations are appropriately formulated for GPUs, it is theorized that the solution efficiency (speed) can be significantly increased while maintaining solution accuracy. The development of this method together with a number of multi-modal unconstrained test problems are tested and presented in this paper
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