Examples of using Parallelization in English and their translations into Serbian
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Array programming is very well suited to implicit parallelization;
The parallelization of software is a significant ongoing topic of research.
The compiler usually conducts two passes of analysis before actual parallelization.
Array programming is very well suited to implicit parallelization; a topic of much research nowadays.
The goal of automatic parallelization is to relieve programmers from the hectic and error-prone manual parallelization process.
The general kernel SVMs can also be solved more efficiently using sub-gradient descent(e.g. P-packSVM[44]),especially when parallelization is allowed.
It can be done automatically by compilers(automatic parallelization) or manually(inserting parallel directives like OpenMP).
Popular optimizations are inline expansion, dead code elimination, constant propagation, loop transformation andeven automatic parallelization.
The second pass attempts to justify the parallelization effort by comparing the theoretical execution time of the code after parallelization to the codes sequential execution time.
Popular optimizations are inline expansion, dead code elimination, constant propagation, loop transformation, register allocation oreven automatic parallelization.
They are: Allow programmers to add"hints" to their programs to guide compiler parallelization, such as HPF for distributed memory systems and OpenMP or OpenHMPP for shared memory systems.
Parallel computing Mainstream parallel programming languages remain either explicitly parallel or(at best) partially implicit,in which a programmer gives the compiler directives for parallelization.
Automatic parallelization of programs continues to remain a technical challenge, but parallel programming models can be used to effectuate a higher degree of parallelism via the simultaneous execution of separate portions of a program on different processors.
Significant part of this research will be hybridization of the mentioned meta-heuristics,combining with exact methods, parallelization and executing on a multi-processor systems.
Automatic parallelization of programs remains a technical challenge, but parallel programming models can be used to effectuate a higher degree of parallelism via the simultaneous execution of separate portions of a program on different processors.[25][26].
However, current parallelizing compilers are not usually capable of bringing out these parallelisms automatically, andit is questionable whether this code would benefit from parallelization in the first place.
Though the quality of automatic parallelization has improved in the past several decades,fully automatic parallelization of sequential programs by compilers remains a grand challenge due to its need for complex program analysis and the unknown factors(such as input data range) during compilation.
It is equivalent to do i= 2, n z(i)= z(1)*2**(i- 1) enddo However, current parallelizing compilers are not usually capable of bringing out these parallelisms automatically,and it is questionable whether this code would benefit from parallelization in the first place.
Automatic parallelization, also auto parallelization, autoparallelization, or parallelization, the last one of which implies automation when used in context, refers to converting sequential code into multi-threaded or vectorized code in order to utilize multiple processors simultaneously in a shared-memory multiprocessor(SMP) machine.
While parallelization and scalability are not considered seriously in conventional DNNs,[175][176][177] all learning for DSNs and TDSNs is done in batch mode, to allow parallelization on a cluster of CPU or GPU nodes.[170][171] Parallelization allows scaling the design to larger(deeper) architectures and data sets.