Most applications process some form of list of commands in an interpreted fashion, and even the series of calls into a framework API can be compiled into a more efficient whole at run-time. But you don't need a full-blown language to observe a huge difference between interpretation and specialization through compilation. As an extreme example, interpreters can execute any kind of program in any language, but by specializing for a specific program you get a compiled version of that program.
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Run-time specialization can have access to the full context in which each routine executes, and although the optimization contribution of specialization for a single parameter is small, the combined speedup can be huge. Various parameters also depend on user input. framework APIs) know very little or nothing about the usage by higher layers. Worse yet, any non-trivial application has a layered architecture in which lower layers (e.g.
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This alone can make it impractical to write fully specialized routines manually, and with the help of metaprogramming it results in code bloat. Systems can have one core or several dozen cores, and many different ISA extensions. However, having a specialized benchmark does not take into account that a typical real-world application deals with many unpredictable conditions. Specialized benchmarks show no advantage of JIT code over static code. Specialization can be done statically, by explicitly writing each variant or by using metaprogramming to generate multiple variants at static compile time, or dynamically by examining the parameters at run-time and generating a specialized path.īecause specialization can be done statically, sometimes aided by metaprogramming, the ability of a JIT-compiler to do it at run-time is often disregarded.
For example when sorting two numbers it is faster to swap them if they are not yet in order, than to call a generic quicksort function. Specialization in general is the use of a more optimal routine that is specific for a certain set of conditions. However, this is rarely achieved in practice. Just-in-time (JIT) compiled code has the potential to be faster than statically compiled code, through run-time specialization.
Reactor also supports control flow constructs and pointer arithmetic with C-like syntax. This is possible through the use of C++ operator overloading. It's the code that when executed will record the calculation to be performed. This is not the code to perform the calculation.
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