Finally (and perhaps most commonly), the data structure can be encoded in a different way, making it smaller, or by physically separating the (small) commonly accessed data from the (large) uncommonly accessed part. This is similar to the first strategy, but it applies to the data structures involved. This applies to obvious cases where something was computed inefficiently in the first place.
#Why pinnacle profiler use so much memory code
First, you can execute less code (which helps cold startup). For data structures having frequently accessed (hot) parts that do not fit in CPU caches (typically they would be larger than several megabytes), a 30 percent reduction in the memory size of hot data typically results in a 10 percent improvement in CPU speed. This reduces the load on the fast caches and makes the program faster. A more feasible technique is to minimize the amount of memory used. In practice, this is only possible in unusual circumstances because ordinarily the program's algorithms dictate the order of memory accesses. If code could be magically rearranged to ensure all memory requests were satisfied in the fast caches, the program would speed up substantially. Since servers run many unrelated programs simultaneously and continuously, servers are application switching constantly this means that memory is almost always an issue for servers. This is similar to the cold startup case except that it affects not just program instructions, but all memory-including memory that was initialized by your application. When the user returns to your app, these stolen pages need to be fetched back from the disk, which makes your app very slow. When your application is reasonably large (larger than 50MB), and a user switches to other applications, these applications steal the physical memory of your application. The final case when memory consumption matters is during application switching. Only memory fetched from the disk (such as the program instructions) affects cold startup memory initialized by the program itself, including all data on the heap and stack, does not affect cold startup. The only way to improve this is to load less data from the disk. For the first (cold) startup, caching has not yet happened and data has to be fetched from disk. This is the reason that an application is faster when launched the second time, during what is called warm startup (the data was cached in faster memory). The operating system tries to mitigate this by caching data from the disk in main memory. As Figure 1 shows, hard disk access is much slower than main memory access. The second case of when (some) memory consumption matters is during an application's cold startup.
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Since the slower memory is slower by an order of magnitude, a few Level-2 cache misses can have a significant performance impact. If hot data paths access more memory, then the operands will frequently need to be fetched from slower memory. At every step deeper into the memory hierarchy, the access time (and size) increases by an order of magnitude or more (hard drives are a 10,000 times slower than RAM) while the cost (per byte) decreases.įigure 1 Size and Access Times with Non-local Storage Figure 1 shows the access time and sizes of the various parts of the memory hierarchy for a typical PC. Next in the hierarchy is the level-2 cache, followed by the main memory (RAM), and finally the hard disk drive. The level-1 (L1) cache is the fastest memory, but is relatively small. Modern processors have a hierarchy of caches to optimize the cost of hardware.
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However, that speed is limited by how long it takes to fetch the operands from memory.
#Why pinnacle profiler use so much memory Pc
A typical PC can execute an instruction in less than half a nanosecond (0.5 ns). The first case of when memory consumption matters is a CPU-intensive application that is manipulating a large amount of data. Lastly, we discuss tools and strategies to determine the memory consumption of your. Next, we discuss the general breakdown of how memory is used in a typical. First, we outline the cases where memory access is a bottleneck and is useful to optimize. In this article, we discuss the basics of memory optimization for. Thus, memory usage can have a direct impact on how fast an application executes and is an important metric to optimize. The execution of instructions is cheap for modern computer hardware while the fetching of instruction operands is expensive. Performance optimization is about one thing: making computer programs run faster. NET Applicationsīy Subramanian Ramaswamy | June 2009 Contents Volume 24 Number 06 CLR Inside Out - Memory Usage Auditing For.