It may seem hard to improve upon the linear scaling of this problem, but quite remarkably, there is a way.
这个问题看起来很难线性地提高其速度，但是值得注意的是，的确有这样一种方法。
As the green linear scaling line in Figure 3 shows, each time you add a processor to your solution, you get X more throughput.
如图3所示的绿色线性扩展线，每当向解决方案添加一个处理器时，您会多得到X的吞吐量。
It's important to know that this doesn't imply that EVERY deployment of an elastic infrastructure will provide the overall application with linear scaling as resources are added.
非常重要的一点是，需要知道这并不意味着每一个弹性的基础架构部署都可以在添加资源时为整个应用程序提供线性伸缩。
Can I replicate the systems to achieve linear scaling?
可以复制系统来实现线性扩展吗？
This implies the possibility of true linear scaling.
这意味着线性伸缩的可能。
This is what is meant by linear scaling.
这就是所谓的线性扩展。
Partitioning enables linear scaling (the green line in Figure 3).
分区支持线性扩展(图3中的绿线)。
A key to linear scaling out is the uniform distribution of data amongst the partitions.
线性扩展的键是在分区上均匀分布的数据。
The new computer cluster on the need to achieve nearly linear scaling.
新的计算机集群上需要实现近似线性的缩放。
In general though, linear scaling DFT is still quite rare and DFT calculations on a routine basis typically involve a few hundreds or thousands of atoms.
通常情况下，线性缩放密度泛函理论的应用还是很少的，而密度泛函理论只能计算成百、成千的原子电子结构。
This paper presents a infeasible interior-point primal -dual affine scaling algorithm for linear programming. it is shown that the method is polynomial-time algorithm.
摘要本文对线性规划提出了一个不可行内点原始-对偶仿射尺度算法，并证明了算法是一个多项式时间算法。
Meanwhile, the linear scaling algorithms of DFT are getting mature, which makes the application of DFT to large systems such as biological macro molecules become possible.
与此同时，线性标度的密度泛函理论算法日趋成熟，使得通过密度泛函理论研究诸如生物大分子之类的体系成为可能。
Using linear programming technique and scaling kernel function, the support vector regression model was obtained.
通过线性规划技术和采用尺度函数作为核函数来实现支持向量回归模型。
The scaling exponents are much smaller than that of linear polymers for both short chains and long chains.
无论是长链还是短链，其标度指数在数值上都比线型高分子的结果要小。
We present an affine scaling trust region algorithm with interior back - tracking and subspace techniques for nonlinear optimizations subject to linear inequality constraints.
使用仿射变换内点回代技术的信赖域子空间算法解线性不等式约束的非线性优化问题。
A linear scaling(LS) based dynamic programming(DP) algorithm was developed for accurate matching of queries by humming.
提出一种用于哼唱识别精确匹配的线性伸缩动态规划算法。
Most multiprocessor machines can get close to linear scaling with a finite number of CPUs, but after a certain point each additional CPU can increase performance overall, but not proportionately.
大多数多处理器的机器在有限的CPU数量的情形下接近线性伸缩度，但是在某点之后每个另外的CPU能总起来增加性能，但不均衡。
The algorithm employs crossover operations and a hybrid scaling method to combines both linear and sequencing-based nonlinear scaling methods.
该算法使用了有效的交叉操作，并设计了将线性定标与基于排序的非线性定标相结合的混合适应值定标方法。
Resilience scaling has been modified for linear returns, as opposed to increasing returns.
韧性的增益方式已经改为了线性，而非原来的递增性。
Resilience scaling has been modified for linear returns, as opposed to increasing returns.
韧性的增益方式已经改为了线性，而非原来的递增性。
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