Data-dependent iterations

Convolution Method A point source is used to measure the scatter function, which is then used to convolve with the source distribution to give an estimation of scatter data. An iterative method is used so that after each iteration, the scatter estimate is improved. The best estimates of scatter events are scaled and then subtracted from the measured image data. The measured scatter function takes into account its dependence on the location and size of the source, detection angle, etc. and thus improves the scatter estimation. This method is computationally efficient because of the availability of fast algorithms. 

A typical approach to HX-MS involves creating the peptide list from data-dependent LC-MS/MS runs applied to a nondeuterated protein digest and searching the data against the protein sequence with proteomics-grade search engines such as Mascot or XITandem (see Eigure 3.1). It is quite effective to iterate such experiments a few times to maximize the list of identifiable peptides, because the... 

The system chooses a set of operators to evaluate, based on data dependencies and functional unit availability. These operators are scheduled and bound, and another set of operators is chosen for the next iteration. Note that this also allows a partially-specified design to be completed by the synthesis system. 

You are using BC to loop pipeline the following code segment. BC is unable to pipeline this loop due to data dependency of variable "mem" from one iteration to the next. 

The concept of loop folding is very similar to that of function pipelining. The only difference is that in loop folding, there exists data dependencies across loop iterations, while in function pipelining, there is no data dependency between instances. 

We also have studied briefly the effects of increasing the depths of loop nests on the slicing time. This is interesting since loops will necessitate conventional fixed-point iterations in the RD data dependency analysis used by PDG-based slicing, whereas our SLV-based method uses a different mechanism to handle loops. 

HardwareC supports a single-in, single-out control flow. This implies that no gotos, breaks from loops, and returns from procedures (other than at the very end) are allowed. It supports the usual iterative and branching control-flow constructs. Iteration can be specified eitha as fixed-bound loops (for loop) or data-dependent loops (while or repeat-until loops), depending on whether the loop exit condition is known at compile time. 

At the completion of a data-dependent loop body. If u is modified in the body of the loop, then the final value it assumes must be loaded into the register reg before the next iteration of the loop. Returning to the previous example, the resulting code segment becomes... 

The execution delay of each vertex must be associated with a particular input sequence when there are vertices in the graph with data-dependent delay. The reason is because the time required to achieve synchronization and the number of iterations necessary for a data-dependent loop are known only in the context of a given input sequence. 

Loop Vertex - A loop executes its body until the exit condition (exitjcond) becomes true. The generation of the exit condition depends on whether the loop bounds are fixed or data-dependent. For fixed iteration loops, a straightforward approach is to use a counter that steps through each iteration of the bounded loop. For the more general case of data-dependent loops, exitjcond is an expression whose value may change dynamically. In particular, exit from a repeat-until loop takes place when both exitjcond is true and the loop body has finished execution, as shown below. 

The primary purpose for expressing experimental data through model equations is to obtain a representation that can be used confidently for systematic interpolations and extrapolations, especially to multicomponent systems. The confidence placed in the calculations depends on the confidence placed in the data and in the model. Therefore, the method of parameter estimation should also provide measures of reliability for the calculated results. This reliability depends on the uncertainties in the parameters, which, with the statistical method of data reduction used here, are estimated from the parameter variance-covariance matrix. This matrix is obtained as a last step in the iterative calculation of the parameters. 

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