11,221 research outputs found
GeNN: a code generation framework for accelerated brain simulations
Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ.
GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials,
Wiki, in-depth example projects and all other related information can be found on the project website http://genn-team.github.io/genn/
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Investigating the impact of image content on the energy efficiency of hardware-accelerated digital spatial filters
Battery-operated low-power portable computing devices are becoming an inseparable part of human daily life. One of the major goals is to achieve the longest battery life in such a device. Additionally, the need for performance in processing multimedia content is ever increasing. Processing image and video content consume more power than other applications. A widely used approach to improving energy efficiency is to implement the computationally intensive functions as digital hardware accelerators. Spatial filtering is one of the most commonly used methods of digital image processing. As per the Fourier theory, an image can be considered as a two-dimensional signal that is composed of spatially extended two-dimensional sinusoidal patterns called gratings. Spatial frequency theory states that sinusoidal gratings can be characterised by its spatial frequency, phase, amplitude, and orientation. This article presents results from our investigation into assessing the impact of these characteristics of a digital image on the energy efficiency of hardware-accelerated spatial filters employed to process the same image. Two greyscale images each of size 128 × 128 pixels comprising two-dimensional sinusoidal gratings at maximum spatial frequency of 64 cycles per image orientated at 0° and 90°, respectively, were processed in a hardware implemented Gaussian smoothing filter. The energy efficiency of the filter was compared with the baseline energy efficiency of processing a featureless plain black image. The results show that energy efficiency of the filter drops to 12.5% when the gratings are orientated at 0° whilst rises to 72.38% at 90°
Analytic Performance Modeling and Analysis of Detailed Neuron Simulations
Big science initiatives are trying to reconstruct and model the brain by
attempting to simulate brain tissue at larger scales and with increasingly more
biological detail than previously thought possible. The exponential growth of
parallel computer performance has been supporting these developments, and at
the same time maintainers of neuroscientific simulation code have strived to
optimally and efficiently exploit new hardware features. Current state of the
art software for the simulation of biological networks has so far been
developed using performance engineering practices, but a thorough analysis and
modeling of the computational and performance characteristics, especially in
the case of morphologically detailed neuron simulations, is lacking. Other
computational sciences have successfully used analytic performance engineering
and modeling methods to gain insight on the computational properties of
simulation kernels, aid developers in performance optimizations and eventually
drive co-design efforts, but to our knowledge a model-based performance
analysis of neuron simulations has not yet been conducted.
We present a detailed study of the shared-memory performance of
morphologically detailed neuron simulations based on the Execution-Cache-Memory
(ECM) performance model. We demonstrate that this model can deliver accurate
predictions of the runtime of almost all the kernels that constitute the neuron
models under investigation. The gained insight is used to identify the main
governing mechanisms underlying performance bottlenecks in the simulation. The
implications of this analysis on the optimization of neural simulation software
and eventually co-design of future hardware architectures are discussed. In
this sense, our work represents a valuable conceptual and quantitative
contribution to understanding the performance properties of biological networks
simulations.Comment: 18 pages, 6 figures, 15 table
NVIDIA Tensor Core Programmability, Performance & Precision
The NVIDIA Volta GPU microarchitecture introduces a specialized unit, called
"Tensor Core" that performs one matrix-multiply-and-accumulate on 4x4 matrices
per clock cycle. The NVIDIA Tesla V100 accelerator, featuring the Volta
microarchitecture, provides 640 Tensor Cores with a theoretical peak
performance of 125 Tflops/s in mixed precision. In this paper, we investigate
current approaches to program NVIDIA Tensor Cores, their performances and the
precision loss due to computation in mixed precision.
Currently, NVIDIA provides three different ways of programming
matrix-multiply-and-accumulate on Tensor Cores: the CUDA Warp Matrix Multiply
Accumulate (WMMA) API, CUTLASS, a templated library based on WMMA, and cuBLAS
GEMM. After experimenting with different approaches, we found that NVIDIA
Tensor Cores can deliver up to 83 Tflops/s in mixed precision on a Tesla V100
GPU, seven and three times the performance in single and half precision
respectively. A WMMA implementation of batched GEMM reaches a performance of 4
Tflops/s. While precision loss due to matrix multiplication with half precision
input might be critical in many HPC applications, it can be considerably
reduced at the cost of increased computation. Our results indicate that HPC
applications using matrix multiplications can strongly benefit from using of
NVIDIA Tensor Cores.Comment: This paper has been accepted by the Eighth International Workshop on
Accelerators and Hybrid Exascale Systems (AsHES) 201
Many-core compiler fuzzing
We address the compiler correctness problem for many-core systems through novel applications of fuzz testing to OpenCL compilers. Focusing on two methods from prior work, random differential testing and testing via equivalence modulo inputs (EMI), we present several strategies for random generation of deterministic, communicating OpenCL kernels, and an injection mechanism that allows EMI testing to be applied to kernels that otherwise exhibit little or no dynamically-dead code. We use these methods to conduct a large, controlled testing campaign with respect to 21 OpenCL (device, compiler) configurations, covering a range of CPU, GPU, accelerator, FPGA and emulator implementations. Our study provides independent validation of claims in prior work related to the effectiveness of random differential testing and EMI testing, proposes novel methods for lifting these techniques to the many-core setting and reveals a significant number of OpenCL compiler bugs in commercial implementations
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