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The runtime has a performance optimization built into it that can overwhelm the measurement of an individual call. Here's an example scenario that demonstrates this problem. Consider the following render sequence:

Example 1: Simple Render Sequence

Looking at the results for the two calls in the render sequence, an instrumenting profiler could return results similar to these:

The profiler returns the number of CPU cycles required to process the work associated with each call (remember that the GPU isn't included in these numbers because the GPU hasn't started working on these commands yet). Because IDirect3DDevice9::DrawPrimitive required almost a million cycles to process, you could conclude that it is not very efficient. However, you'll soon see why this conclusion is incorrect and how you can generate results that can be used for budgeting.


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, or Clear (such as SetTexture , SetVertexDeclaration , and SetRenderState ) produce a state change. Each state change sets pipeline state that controls how rendering will be done.

DrawIndexedPrimitive SetTexture SetVertexDeclaration SetRenderState

Optimizations in the runtime and/or the driver are designed to speed up rendering by reducing the amount of work required. The following are a couple of state change optimizations that may pollute profile averages:

There is no foolproof way to look at a render sequence and conclude which state changes will set a dirty bit and defer work, or will simply be removed by optimization. Even if you could identify optimized state changes in today's runtime or driver, tomorrow's runtime or driver is likely to be updated. You also don't readily know what the previous state was so it is difficult to identify redundant state changes. The only way to verify the cost of a state change is to measure the render sequence that includes the state changes.

As you can see, the complications caused by having multiple processors, commands being processed by more than one component, and optimizations built into the components make profiling difficult to predict. In the next section, each of these profiling challenges will be addressed. Sample Direct3D render sequences will be shown, with the accompanying measurement techniques. With this knowledge, you will be able to generate accurate, repeatable measurements on individual calls.

Now that some of the profiling challenges have been highlighted, this section will show you techniques that will help you generate profile measurements that can be used for budgeting. Accurate, repeatable profiling measurements are possible if you understand the relationship between the components controlled by the CPU, and how to avoid performance optimizations implemented by the runtime and the driver.

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Rusnak RA, Borer JM, Fastow JS. Misdiagnosis of acute appendicitis: common features discovered in cases after litigation. . 1994 Jul. 12(4):397-402. .

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Stussman BJ. National Hospital Ambulatory Medical Care Survey: 1994 emergency department summary. . 1996 May 17. 1-20. .

Vermeulen B, Morabia A, Unger PF, et al. Acute appendicitis: influence of early pain relief on the accuracy of clinical and US findings in the decision to operate--a randomized trial. . 1999 Mar. 210(3):639-43. .

Vissers RJ, Abu-Laban RB, McHugh DF. Amylase and lipase in the emergency department evaluation of acute pancreatitis. . 1999 Nov-Dec. 17(6):1027-37. .

Wildermuth S, Leschka S, Alkadhi H, et al. Multislice CT in the pre- and postinterventional evaluation of mesenteric perfusion. . 2005 Jun. 15(6):1203-10. .

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Ukkonen M, Kivivuori A, Rantanen T, Paajanen H. Emergency abdominal operations in the elderly: a multivariate regression analysis of 430 consecutive patients with acute abdomen. . 2015 Dec. 39 (12):2854-61. .

Dimou FM, Adhikari D, Mehta HB, Riall TS. Outcomes in older patients with grade III cholecystitis and cholecystostomy tube placement: a propensity score analysis. . 2017 Jan 6. .

Media Gallery
Inflammatory mass in the right lower quadrant of an 84-year-old woman with mild abdominal pain of 2 days' duration. A ruptured appendix was found at surgery.
CT scan of a 76-year-old woman with severe abdominal pain of 3 hours' duration. Note the ringlike enhancement of bowel wall in the posterior abdomen. Ischemic small bowel was resected at surgery.
Radiograph of a 90-year-old man with abdominal pain of 4 days' duration. Plain films reveal large bowel dilatation. Sigmoid volvulus with ischemic colon was diagnosed at surgery.
Radiograph of a 79-year-old woman with several hours of diffuse abdominal pain. Initial examination of the plain films suggests bowel obstruction.
Radiograph of a 79-year-old woman with several hours of diffuse abdominal pain. Initial examination of the plain films suggests bowel obstruction. Close-up view reveals pneumatosis intestinalis, indicating mesenteric ischemia.
CT scan of a 64-year-old woman with vague abdominal pain of 2 days' duration. Physical examination revealed a tender palpable mass in the left lower quadrant. CT scan reveals an incarcerated ventral hernia.
CT scan of a 62-year-old man who reported 2 weeks of left lower quadrant abdominal pain. CT scan reveals fat stranding and multiple diverticula around the descending colon. A phlegmon containing bowel and inflammatory tissue has eroded into the left psoas muscle.
A lower CT scan slice from a 62-year-old man who reported 2 weeks of left lower quadrant abdominal pain. Multiple diverticula are observed with an inflammatory mass overlying the left ilium.
Contributor Information and Disclosures

E David Bryan, MD Assistant Professor of Emergency Medicine, Texas Tech University Health Science Center at El Paso; Medical Director, Department of Emergency Medicine, Thomason Hospital E David Bryan, MD is a member of the following medical societies: 2018 autumn man casual shoe mesh upper sneakers sport shoes Sale Great Deals Websites Online Clearance Deals jaH8k0iim
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Source code: Lib/inspect.py

Source code:

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module provides several useful functions to help get information about live objects such as modules, classes, methods, functions, tracebacks, frame objects, and code objects. For example, it can help you examine the contents of a class, retrieve the source code of a method, extract and format the argument list for a function, or get all the information you need to display a detailed traceback.

There are four main kinds of services provided by this module: type checking, getting source code, inspecting classes and functions, and examining the interpreter stack.

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function retrieves the members of an object such as a class or module. The functions whose names begin with “is” are mainly provided as convenient choices for the second argument to getmembers() . They also help you determine when you can expect to find the following special attributes:

Changed in version 3.5: Add and attributes to generators.

The attribute of generators is now set from the function name, instead of the code name, and it can now be modified.

Changed in version 3.7: Add attribute to coroutines.

Return all the members of an object in a list of (name, value) pairs sorted by name. If the optional predicate argument is supplied, only members for which the predicate returns a true value are included.


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will only return class attributes defined in the metaclass when the argument is a class and those attributes have been listed in the metaclass’ custom .

Return the name of the module named by the file path , without including the names of enclosing packages. The file extension is checked against all of the entries in importlib.machinery.all_suffixes() . If it matches, the final path component is returned with the extension removed. Otherwise, None is returned.

Note that this function only returns a meaningful name for actual Python modules - paths that potentially refer to Python packages will still return None .

Changed in version 3.3: The function is based directly on .

Return true if the object is a module.

Return true if the object is a class, whether built-in or created in Python code.

Return true if the object is a bound method written in Python.