Each tensor core is a hardware-implemented function that performs a matrix multiply accumulate (MMA) operation of 4 × 4 matrices in one GPU clock cycleĪdapting any arbitrary algorithm to a tensor core scheme is not a trivial task, as tensor cores are different from regular GPU cores. In comparison, the traditional CUDA cores, which are 5120 in total for the GPUs recently mentioned, offer up to ∼ 15 TFLOPS of performance in FP32 precision and around ∼ 7 TFLOPS in FP64 precision.Īs of January 2020, GPUs contain up to 640 tensor cores that can work in parallel. Today, the Nvidia Volta GPU Tesla V100, Quadro V100 and Titan V all include around 640 tensor cores, and they can offer up to 120 TFLOPS in mixed FP16-FP32 precision. In terms of numerical error, in the normal distribution test (bottom left) all variants present less than 1% of numerical error with respect to the CPU reduction, once the input size is n ≥ 10 × 10⁶ numbers. One important non-Machine Learning computational pattern is the arithmetic reduction, which is one of the most used patterns in science and technology, i.e., it is the discrete integration tool for modelling many scientific phenomena, from n-body/Monte Carlo simulations, , cellular automata to map-reduce workloads and ray tracing, among many others. The results obtained in this work show that tensor cores can indeed provide a significant performance improvement to non-Machine Learning applications such as the arithmetic reduction, which is an integration tool for studying many scientific phenomena. Reduction of $n$ numbers as a set of chained $m \times m$ matrix multiplyĪccumulate (MMA) operations executed in parallel by GPU tensor cores. This work proposes a GPU tensor core approach that encodes the arithmetic You may not need double-precision in the Tensor cores, here reduction with CPUs using double-precision is compared to Nvidia where the Tensor cores do not have that capability (but rest of the chip has it and the CUDA cores are used to, it seems to me for the double-precision capability): GPU Tensor Cores for fast Arithmetic Reductions In theory some future chips with Tensor cores could have double-precision capability while the trend is in the other direction, smaller data types, rather more cores and more efficient use of memory, with just released Nvidia GPUs now close(er) to 6000 CUDA cores than to 5000 (and just release one with just released one with 80 GB of memory). Very likely Tensor cores in Nvidia chips do not have double-precision (and neither Google’s TPU, that I believe are similar), and the absence may not be important as as I wouldn’t rule out mixed 16-, 32-, 64-bit computation as done in some cases with help of non-Tensor cores. All rights reserved.EDIT: New Ampere Nvidia GPUs do have Tensor Cores capable of double-precision but see my follow-up post(s) on it, and why you still do not want to be limited to double. It is advised that waveforms be visually inspected prior to conducting acoustic analysis, and that voice outcomes not be combined or compared across AASPs.Īcoustic voice analysis Vocal perturbation Voice disorders.Ĭopyright © 2022 The Voice Foundation. The variation observed across programs calls into question the validity in comparing voice outcomes reported by one AASP to those previously obtained by another, particularly for acoustic signals with aperiodic components that are commonly present in disordered voices. Similar, but magnified patterns of results were observed for speakers with dysphonia. The present study replicated previous findings of interprogram differences for healthy speakers, with MDVP consistently yielding higher values than Praat and TF32 for SD F0, jitter, and shimmer and lower values for HNR. Descriptive, inferential, and correlation data are reported for mean fundamental frequency (mean F0), standard deviation of fundamental frequency (SD F0), short-term perturbation measures of jitter and shimmer, and harmonic-to-noise ratio (HNR). Sustained vowel phonations for the quantal vowels /ɑ/, /i/, and /u/ were analyzed for 80 speakers with organic dysphonia and 60 age- and sex-matched healthy controls. In the present study, five acoustic measures were compared across healthy speakers and speakers with dysphonia for three AASPs commonly used in research, education, and clinical practice: Multidimensional Voice Program (MDVP) by Computerized Speech Lab, Praat, and TF32. The reported voice data, however, are sensitive to the algorithm used by each acoustic analysis software program (AASP) to analyze the corresponding waveform. Instrumental voice assessment plays a critical role in identifying vocal issues and for documenting treatment outcomes.
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