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C, C++, Fortran/Fortran90 and Python applications. Performance profiler. Shows I/O, communication, floating point operation usage and memory access costs. Supports multi-threaded and multi-process applications - such as those with MPI or OpenMP parallelism and scales to very high node counts. Proprietary CodeAnalyst by AMD: Linux, Windows
Choice of design depends on the goals: when designing a compiler, if fast compilation is the key priority, a one-pass compiler is faster than a multi-pass compiler (assuming same work), but if speed of output code is the goal, a slower multi-pass compiler fulfills the goal better, even though it takes longer itself. Choice of platform and ...
It was a design choice from the start to only include very simple toy problems, each providing a different kind of programming challenge. [3] This provides users of the Benchmark Game the opportunity to scrutinize the various implementations. [4] binary-trees; chameneos-redux; fannkuch-redux; fasta; k-nucleotide; mandelbrot; meteor-contest; n ...
A profiler can be applied to an individual method or at the scale of a module or program, to identify performance bottlenecks by making long-running code obvious. [1] A profiler can be used to understand code from a timing point of view, with the objective of optimizing it to handle various runtime conditions [2] or various loads. [3]
Self-modifying code can be rewritten as code that tests a flag and branches to alternative sequences based on the outcome of the test, but self-modifying code typically runs faster. Self-modifying code conflicts with authentication of the code and may require exceptions to policies requiring that all code running on a system be signed.
A CPU designer is often required to implement a particular instruction set, and so cannot change N. Sometimes a designer focuses on improving performance by making significant improvements in f (with techniques such as deeper pipelines and faster caches), while (hopefully) not sacrificing too much C—leading to a speed-demon CPU design.
Infrastructure as Code (IaC) allows you to manage servers and their configurations using code. There are two ways to send these configurations to servers: the ' push ' and ' pull ' methods. In the 'push' method, the system controlling the configuration directly sends instructions to the server.
Concurrency of Python code can only be achieved with separate CPython interpreter processes managed by a multitasking operating system. This complicates communication between concurrent Python processes , though the multiprocessing module mitigates this somewhat; it means that applications that really can benefit from concurrent Python-code ...