Although this technique proved to be successful for aligning with the small Arabidopsis Col-0 genome, its high memory requirement makes its applicability to a human genome still questionable (Li and Homer, 2010).
The performance of the proposed receiver is approximately within 1 dB of a similar system employing DFE and turbo code, however, at a significantly reduced computational complexity and memory requirements, making our system attractive for real-time implementation.
These recursive operations with few memory requirements make this algorithm easy to implement on our architecture.
Our new algorithm thus has the significant advantage of linearizing the memory requirement and making it independent of the sequence length for HMMs while increasing the time requirement only by a factor of M K M + K, i.e. decreasing it when only one state path K = 1 is sampled.
The word-monitoring task minimizes memory requirements by making the target word available throughout the trial and measuring comprehension online.
We explained before that this sort of approach has considerable memory and computational requirements, making real-time implementation difficult.
The access requirement makes these much more limited than the likes of Meltdown and Spectre, which exploited problems at the memory handling and architecture level.
The significant computational work and memory requirement of these methods makes a numerical simulation of three-dimensional space-fractional diffusion equations computationally prohibitively expensive.
Before going into details of the implementation and giving complexity estimates for the individual step as well as upper bounds for the memory requirements, let us make some general remarks: The presented algorithms are independent of the architecture used for the actual computations, i.e. apart from some implementation details, the same algorithms are used for CPU and GPU implementations.
However, in implementations such as high-resolution cyclic spectrum estimation by the order of N × N, the memory requirements are quite large, making cyclic spectrum analysis especially difficult.
