scholarly journals Autotuning Parallel Programs by Model Checking

2021 ◽  
Vol 28 (4) ◽  
pp. 338-355
Author(s):  
Natalia Olegovna Garanina ◽  
Sergei Petrovich Gorlatch
Keyword(s):  
Model Checking ◽  
Graphics Processing Units ◽  
Formal Semantics ◽  
Parallel Programs ◽  
Tuning Parameters ◽  
Counter Example ◽  
Optimality Property ◽  
Execution Model ◽  
Parallel Graphics ◽  
Graphics Processing

The paper presents a new approach to autotuning data-parallel programs. Autotuning is a search for optimal program settings which maximize its performance. The novelty of the approach lies in the use of the model checking method to find the optimal tuning parameters by the method of counterexamples. In our work, we abstract from specific programs and specific processors by defining their representative abstract patterns. Our method of counterexamples implements the following four steps. At the first step, an execution model of an abstract program on an abstract processor is described in the language of a model checking tool. At the second step, in the language of the model checking tool, we formulate the optimality property that depends on the constructed model. At the third step, we find the optimal values of the tuning parameters by using a counterexample constructed during the verification of the optimality property. In the fourth step, we extract the information about the tuning parameters from the counter-example for the optimal parameters. We apply this approach to autotuning parallel programs written in OpenCL, a popular modern language that extends the C language for programming both standard multi-core processors (CPUs) and massively parallel graphics processing units (GPUs). As a verification tool, we use the SPIN verifier and its model representation language Promela, whose formal semantics is good for modelling the execution of parallel programs on processors with different architectures.

scholarly journals A Low-Power Spike-Like Neural Network Design

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1479 ◽  
Author(s):  
Michael Losh ◽  
Daniel Llamocca
Keyword(s):  
Neural Network ◽  
Graphics Processing Units ◽  
Hybrid Network ◽  
Network Resource ◽  
Performance Requirements ◽  
Reduction Methods ◽  
Sum Of Products ◽  
Parallel Graphics ◽  
Hardware Platforms ◽  
Graphics Processing

Modern massively-parallel Graphics Processing Units (GPUs) and Machine Learning (ML) frameworks enable neural network implementations of unprecedented performance and sophistication. However, state-of-the-art GPU hardware platforms are extremely power-hungry, while microprocessors cannot achieve the performance requirements. Biologically-inspired Spiking Neural Networks (SNN) have inherent characteristics that lead to lower power consumption. We thus present a bit-serial SNN-like hardware architecture. By using counters, comparators, and an indexing scheme, the design effectively implements the sum-of-products inherent in neurons. In addition, we experimented with various strength-reduction methods to lower neural network resource usage. The proposed Spiking Hybrid Network (SHiNe), validated on an FPGA, has been found to achieve reasonable performance with a low resource utilization, with some trade-off with respect to hardware throughput and signal representation.

scholarly journals Review of smoothed particle hydrodynamics: towards converged Lagrangian flow modelling

2020 ◽  
Vol 476 (2241) ◽  
pp. 20190801
Author(s):  
Steven J. Lind ◽  
Benedict D. Rogers ◽  
Peter K. Stansby
Keyword(s):  
Smoothed Particle Hydrodynamics ◽  
Graphics Processing Units ◽  
Wave Structure ◽  
Free Form ◽  
Mesh Free ◽  
Weakly Compressible ◽  
Particle Hydrodynamics ◽  
Massively Parallel Computing ◽  
Smoothed Particle ◽  
Graphics Processing

This paper presents a review of the progress of smoothed particle hydrodynamics (SPH) towards high-order converged simulations. As a mesh-free Lagrangian method suitable for complex flows with interfaces and multiple phases, SPH has developed considerably in the past decade. While original applications were in astrophysics, early engineering applications showed the versatility and robustness of the method without emphasis on accuracy and convergence. The early method was of weakly compressible form resulting in noisy pressures due to spurious pressure waves. This was effectively removed in the incompressible (divergence-free) form which followed; since then the weakly compressible form has been advanced, reducing pressure noise. Now numerical convergence studies are standard. While the method is computationally demanding on conventional processors, it is well suited to parallel processing on massively parallel computing and graphics processing units. Applications are diverse and encompass wave–structure interaction, geophysical flows due to landslides, nuclear sludge flows, welding, gearbox flows and many others. In the state of the art, convergence is typically between the first- and second-order theoretical limits. Recent advances are improving convergence to fourth order (and higher) and these will also be outlined. This can be necessary to resolve multi-scale aspects of turbulent flow.

Adaptive Precision Block-Jacobi for High Performance Preconditioning in the Ginkgo Linear Algebra Software

2021 ◽  
Vol 47 (2) ◽  
pp. 1-28
Author(s):  
Goran Flegar ◽  
Hartwig Anzt ◽  
Terry Cojean ◽  
Enrique S. Quintana-Ortí
Keyword(s):  
Linear Algebra ◽  
Graphics Processing Units ◽  
High Performance ◽  
Numerical Algorithms ◽  
Mixed Precision ◽  
Before And After ◽  
Memory Accesses ◽  
Specialized Hardware ◽  
The Individual ◽  
Graphics Processing

The use of mixed precision in numerical algorithms is a promising strategy for accelerating scientific applications. In particular, the adoption of specialized hardware and data formats for low-precision arithmetic in high-end GPUs (graphics processing units) has motivated numerous efforts aiming at carefully reducing the working precision in order to speed up the computations. For algorithms whose performance is bound by the memory bandwidth, the idea of compressing its data before (and after) memory accesses has received considerable attention. One idea is to store an approximate operator–like a preconditioner–in lower than working precision hopefully without impacting the algorithm output. We realize the first high-performance implementation of an adaptive precision block-Jacobi preconditioner which selects the precision format used to store the preconditioner data on-the-fly, taking into account the numerical properties of the individual preconditioner blocks. We implement the adaptive block-Jacobi preconditioner as production-ready functionality in the Ginkgo linear algebra library, considering not only the precision formats that are part of the IEEE standard, but also customized formats which optimize the length of the exponent and significand to the characteristics of the preconditioner blocks. Experiments run on a state-of-the-art GPU accelerator show that our implementation offers attractive runtime savings.

scholarly journals DSPSR: Digital Signal Processing Software for Pulsar Astronomy

2011 ◽  
Vol 28 (1) ◽  
pp. 1-14 ◽  
Author(s):  
W. van Straten ◽  
M. Bailes
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Graphics Processing Units ◽  
High Performance ◽  
Digital Signal ◽  
General Purpose ◽  
Design Decisions ◽  
Extensive Range ◽  
Processing Software ◽  
Graphics Processing

Abstractdspsr is a high-performance, open-source, object-oriented, digital signal processing software library and application suite for use in radio pulsar astronomy. Written primarily in C++, the library implements an extensive range of modular algorithms that can optionally exploit both multiple-core processors and general-purpose graphics processing units. After over a decade of research and development, dspsr is now stable and in widespread use in the community. This paper presents a detailed description of its functionality, justification of major design decisions, analysis of phase-coherent dispersion removal algorithms, and demonstration of performance on some contemporary microprocessor architectures.

Practical acceleration of direct whole-core calculation employing graphics processing units

2021 ◽  
Vol 133 ◽  
pp. 103631
Author(s):  
Namjae Choi ◽  
Junsu Kang ◽  
Han Gyu Lee ◽  
Han Gyu Joo
Keyword(s):  
Graphics Processing Units ◽  
Graphics Processing
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