Brain encoding with neuroimaging data is an established analysis aimed at
predicting human brain activity directly from complex stimuli features such as
movie frames. Typically, these features are the latent space representation
from an artificial neural network, and the stimuli are image, audio, or text
inputs. Ridge regression is a popular prediction model for brain encoding due
to its good out-of-sample generalization performance. However, training a ridge
regression model can be highly time-consuming when dealing with large-scale
deep functional magnetic resonance imaging (fMRI) datasets that include many
space-time samples of brain activity. This paper evaluates different
parallelization techniques to reduce the training time of brain encoding with
ridge regression on the CNeuroMod Friends dataset, one of the largest deep fMRI
resource currently available. With multi-threading, our results show that the
Intel Math Kernel Library (MKL) significantly outperforms the OpenBLAS library,
being 1.9 times faster using 32 threads on a single machine. We then evaluated
the Dask multi-CPU implementation of ridge regression readily available in
scikit-learn (MultiOutput), and we proposed a new "batch" version of Dask
parallelization, motivated by a time complexity analysis. In line with our
theoretical analysis, MultiOutput parallelization was found to be impractical,
i.e., slower than multi-threading on a single machine. In contrast, the
Batch-MultiOutput regression scaled well across compute nodes and threads,
providing speed-ups of up to 33 times with 8 compute nodes and 32 threads
compared to a single-threaded scikit-learn execution. Batch parallelization
using Dask thus emerges as a scalable approach for brain encoding with ridge
regression on high-performance computing systems using scikit-learn and large
fMRI datasets.

评估不同的并行化技术以减少在 CNeuroMod Friends 数据集上使用 ridge 回归进行脑编码的训练时间，结果显示 Intel Math Kernel Library 在单个机器上使用 32 个线程时比 OpenBLAS 库快 1.9 倍。使用 Dask 进行批处理并行化的 Batch-MultiOutput 回归在计算节点和线程上扩展得很好，在 8 个计算节点和 32 个线程相对于单线程的情况下加速了 33 倍，这种方法在高性能计算系统上使用 scikit-learn 和大型 fMRI 数据集进行脑编码具有可扩展性。

大规模个体 fMRI 数据集中脑编码的岭回归扩展

Scaling up ridge regression for brain encoding in a massive individual  fMRI dataset

The current era of quantum computing has yielded several algorithms that
promise high computational efficiency. While the algorithms are sound in theory
and can provide potentially exponential speedup, there is little guidance on
how to design proper quantum circuits to realize the appropriate unitary
transformation to be applied to the input quantum state. In this paper, we
present \texttt{QArchSearch}, an AI based quantum architecture search package
with the \texttt{QTensor} library as a backend that provides a principled and
automated approach to finding the best model given a task and input quantum
state. We show that the search package is able to efficiently scale the search
to large quantum circuits and enables the exploration of more complex models
for different quantum applications. \texttt{QArchSearch} runs at scale and high
efficiency on high-performance computing systems using a two-level
parallelization scheme on both CPUs and GPUs, which has been demonstrated on
the Polaris supercomputer.

基于 QTensor 库的 AI 量子架构搜索包 QArchSearch 能够高效扩展到大型量子电路，并在不同的量子应用中探索更复杂的模型。通过在 CPU 和 GPU 上采用两级并行化方案，在 Polaris 超级计算机上实现了大规模和高效的搜索。