What Is Dynamic Analysis?
January 1, 2019

What Is Dynamic Analysis?

In complex, high-performance computing (HPC) environments, code debugging is a time-consuming process. Developers use dynamic code analysis to simplify the process. The result is faster delivery of safe, robust applications.

Why Is Dynamic Analysis?

Dynamic analysis is a process that helps software developers find bugs and security issues while software is running. The alternative is static code analysis, which occurs offline or before executing the code.

Why You Need Dynamic Code Analysis Tools

In HPC environments. Supercomputers are running thousands of threads and processes at the same time. This makes it difficult to isolate faulty source code. Different programming languages, platforms, and standards only add to the complexity.

Meanwhile, developers are under tremendous pressure to deliver clean applications faster. Dynamic code analysis tools can help them achieve this.

Dynamic analysis tools simplify the process of troubleshooting, isolating issues, and debugging. They identify potential issues early, which reduces downtime and impact on delivery times. Some tools are built to perform single, specific tasks, on small applications. More robust tools are built for multithreaded, multiprocess environments. Some can even handle mixed-language applications.

The best dynamic code analysis tools fit cleanly into development environments. They offer an easy-to-use graphical user interface (GUI) that makes it easy to control the process.

Why Choose TotalView for Your Dyanmic Code Analysis Tool?

Running an HPC environment? TotalView is the de facto standard for run-time analysis and debugging. It is a source code debugger for troubleshooting complex, multithreaded, or multiprocess programs. TotalView's eash-to-use GUI gives developers unprecedented control over processes and thread execution. It also provides deep visibility into program states and data.

TotalView is built for:

  • Multicore and parallel computing on C, C++, Fortran, and Python.
  • Mixed-language Python and C/C++ applications.
  • Linux, UNIX, and macOS platforms.

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