Deep convolutional neural networks (ConvNets) of 3-dimensional kernels allow
joint modeling of spatiotemporal features. These networks have improved
performance of video and volumetric image analysis, but have been limited in
size due to the low memory ceiling of GPU hardware. Existing CPU
implementations overcome this constraint but are impractically slow. Here we
extend and optimize the faster Winograd-class of convolutional algorithms to
the $N$-dimensional case and specifically for CPU hardware. First, we remove
the need to manually hand-craft algorithms by exploiting the relaxed
constraints and cheap sparse access of CPU memory. Second, we maximize CPU
utilization and multicore scalability by transforming data matrices to be
cache-aware, integer multiples of AVX vector widths. Treating 2-dimensional
ConvNets as a special (and the least beneficial) case of our approach, we
demonstrate a 5 to 25-fold improvement in throughput compared to previous
state-of-the-art.

通过扩展和优化快速 Winograd 级卷积算法，我们在 CPU 硬件上最大化 CPU 利用率及多核可伸缩性，处理了视频和体积图像分析中的空时特征，并证明了与之前的最先进技术相比，吞吐量提高了 5 到 25 倍。

多核上的深层张量卷积

Deep Tensor Convolution on Multicores

Given the growing importance of large-scale graph analytics, there is a need
to improve the performance of graph analysis frameworks without compromising on
productivity. GraphMat is our solution to bridge this gap between a
user-friendly graph analytics framework and native, hand-optimized code.
GraphMat functions by taking vertex programs and mapping them to high
performance sparse matrix operations in the backend. We get the productivity
benefits of a vertex programming framework without sacrificing performance.
GraphMat is in C++, and we have been able to write a diverse set of graph
algorithms in this framework with the same effort compared to other vertex
programming frameworks. GraphMat performs 1.2-7X faster than high performance
frameworks such as GraphLab, CombBLAS and Galois. It achieves better multicore
scalability (13-15X on 24 cores) than other frameworks and is 1.2X off native,
hand-optimized code on a variety of different graph algorithms. Since GraphMat
performance depends mainly on a few scalable and well-understood sparse matrix
operations, GraphMatcan naturally benefit from the trend of increasing
parallelism on future hardware.

GraphMat 是一个高性能的图分析框架，可通过将顶点程序映射到高性能的稀疏矩阵操作来提高图分析框架的效率并且不影响生产力。