Memory profiling is an important technique which aids program optimization and can even help tracking down bugs. The main problem with the current memory profiling techniques and tools is that they slow down the target software considerably therefore making them inadequate for mainline integration. Ideally, the user would be able to monitor memory consumption without having to worry about the rest of the software being affected in any way. This thesis provides a comparison of existing techniques and tools along with the description of a memory profiler implementation which tries to provide a balance between the information it is able to retrieve and the influence it has on the target software.

<p>Excessive memory use may cause severe performance problems and system crashes. Without appropriate tools, it may be difficult or impossible to determine why a program is using too much memory. This applies even though Python provides automatic memory management --- garbage collection can help avoid many memory allocation bugs, but only to a certain extent due to the lack of information during program execution. There is still a need for tools helping the programmer to understand the memory behaviour of programs, especially in complicated situations. The primary motivation for Heapy is that there has been a lack of such tools for Python.</p><p>The main questions addressed by Heapy are how much memory is used by objects, what are the objects of most interest for optimization purposes, and why are objects kept in memory. Memory leaks are often of special interest and may be found by comparing snapshots of the heap population taken at different times. Memory profiles, using different kinds of classifiers that may include retainer information, can provide quick overviews revealing optimization possibilities not thought of beforehand. Reference patterns and shortest reference paths provide different perspectives of object access patterns to help explain why objects are kept in memory.</p>

This report describes the architecture and implementation of a memory profiler for 3D graphics applications. The memory profiling is done for parts of the program which runs on the graphics processor and is responsible for rendering the image. The shaders are parsed and every memory instruction is instrumented with additional instruction for profiling. The results are then transferred from the video memory to CPU memory. Profiling is done for a frame and completes in less than three minutes. The report also describes various analyses that can be done using the results obtained from this profiler. The report discusses the design of an analytical cache model that can be used to identify candidate memory buffers suitable for caching among all the buffers used by an application. The profiler can segregate results for reads and writes separately, can handle all formats of texture access instructions and predicated instructions. / text

Excessive memory use may cause severe performance problems and system crashes. Without appropriate tools, it may be difficult or impossible to determine why a program is using too much memory. This applies even though Python provides automatic memory management --- garbage collection can help avoid many memory allocation bugs, but only to a certain extent due to the lack of information during program execution. There is still a need for tools helping the programmer to understand the memory behaviour of programs, especially in complicated situations. The primary motivation for Heapy is that there has been a lack of such tools for Python. The main questions addressed by Heapy are how much memory is used by objects, what are the objects of most interest for optimization purposes, and why are objects kept in memory. Memory leaks are often of special interest and may be found by comparing snapshots of the heap population taken at different times. Memory profiles, using different kinds of classifiers that may include retainer information, can provide quick overviews revealing optimization possibilities not thought of beforehand. Reference patterns and shortest reference paths provide different perspectives of object access patterns to help explain why objects are kept in memory.