jit | 易学教程

Is it better to store value as variable or call method again?

阅读更多关于 Is it better to store value as variable or call method again?

问题 Recently, I started learning some Java. From what I've already learned about JVM it looks like JIT makes it pretty fast on operations requiring CPU cycles (i.e. calling a method) but also makes it hungry for memory. So when I need same output from same method as before, is it generally better approach to store the output from before in variable and use it again - while holding it in memory all this time - or call the same method again? 回答1: It is better practice to hold the output in a

Are GetCallingAssembly() and GetExecutingAssembly() equally prone to JIT inlining?

阅读更多关于 Are GetCallingAssembly() and GetExecutingAssembly() equally prone to JIT inlining?

问题 There's Assembly.GetExecutingAssembly() and Assembly.GetCallingAssembly(). Note that GetCallingAssembly() has a Remark mentioning that depending on how JIT inlining behaves it may be possible that one method is (or is not) inlined into another and so GetCallingAssembly() returns varying results. Now how is GetExecutingAssembly() different? JIT inlining could technically inline the code that calls GetExecutingAssembly() and so that code now belongs to a different assembly and depending on

Usage of parallel option in numba.jit decoratior makes function give wrong result

阅读更多关于 Usage of parallel option in numba.jit decoratior makes function give wrong result

Given two opposite corners of a rectangle (x1, y1) and (x2, y2) and two radii r1 and r2 , find the ratio of points that lie between the circles defined by the radii r1 and r2 to the total number of points in the rectangle. Simple NumPy approach: def func_1(x1,y1,x2,y2,r1,r2,n): x11,y11 = np.meshgrid(np.linspace(x1,x2,n),np.linspace(y1,y2,n)) z1 = np.sqrt(x11**2+y11**2) a = np.where((z1>(r1)) & (z1<(r2))) fill_factor = len(a[0])/(n*n) return fill_factor Next I tried to optimize this function with the jit decorator from numba. When I use: nopython = True The function is faster and gives the

C# based Windows Service - Tries to do JIT Debugging in production

阅读更多关于 C# based Windows Service - Tries to do JIT Debugging in production

问题 I am getting this error in my event logs for a service I put into production: An unhandled win32 exception occurred in RivWorks.FeedHandler.exe [5496]. Just-In-Time debugging this exception failed with the following error: Debugger could not be started because no user is logged on. I have it installed and running under a Win NT global account. I have no idea why it is trying to drop into debugging mode. It was built under the Release model. Running on the 4.0 Framework. When I run on my dev

Will the JVM ever inline an object's instance variables and methods?

阅读更多关于 Will the JVM ever inline an object's instance variables and methods?

问题 Suppose I have a very tight inner loop, each iteration of which accesses and mutates a single bookkeeping object that stores some simple data about the algorithm and has simple logic for manipulating it The bookkeeping object is private and final and all of its methods are private, final and @inline. Here's an example (in Scala syntax): object Frobnicate { private class DataRemaining(val start: Int, val end: Int) { @inline private def nextChunk = .... } def frobnicate { // ... val bookkeeper

How JIT Compilers Operate

阅读更多关于 How JIT Compilers Operate

问题 JIT compilers, by definition, generate code on the fly for execution. But in, say, Windows, we have all kinds of protection that prevent self modifying code or executing from data memory (DEP). So how is it possible for JIT compilers to generate code on the fly? 回答1: They ask the OS for some memory which is readable, writeable and executable. e.g. you can allocate such memory using mmap() with PROT_READ | PROT_WRITE | PROT_EXEC (POSIX), or VirtualAlloc() with PAGE_EXECUTE_READWRITE (Windows).

Numba: calling jit with explicit signature using arguments with default values

阅读更多关于 Numba: calling jit with explicit signature using arguments with default values

问题 I'm using numba to make some functions containing cycles on numpy arrays. Everything is fine and dandy, I can use jit and I learned how to define the signature. Now I tried using jit on a function with optional arguments, e.g.: from numba import jit import numpy as np @jit(['float64(float64, float64)', 'float64(float64, optional(float))']) def fun(a, b=3): return a + b This works, but if instead of optional(float) I use optional(float64) it doesn't (same thing with int or int64 ). I lost 1

locate corresponding JS source of code which is not optimized by V8

阅读更多关于 locate corresponding JS source of code which is not optimized by V8

问题 I try to optimize the performance of a node.js application and therefore I am analyzing the behavior of V8's JIT compiler. When running the application via node --trace_deopt --trace_opt --code_comments --print_optcode ... , the output contains many recurring lines like the following: [didn't find optimized code in optimized code map for 0x490a8b4aa69 <SharedFunctionInfo>] How can I find out which javascript code corresponds to 0x490a8b4aa69 ? The full output is available here. 回答1: That

Can java inline a large method if the most of it would be dead code at the call site?

阅读更多关于 Can java inline a large method if the most of it would be dead code at the call site?

I know that one of the criteria that Java HotSpot uses to decide whether a method is worth inlining is how large it the method is. On one hand, this seems sensible: if the method is large, in-lining leads to code bloat and the method would take so long to execute that the call overhead is trivial. The trouble with this logic is that it might turn out that AFTER you decide to inline, it becomes clear that for this particular call-site, most of the method is dead code. For instance, the method may be a giant switch statement, but most call sites call the method with a compile-time constant, so

Just in Time Compilation - Storing vs Doing always [duplicate]

阅读更多关于 Just in Time Compilation - Storing vs Doing always [duplicate]

Possible Duplicate: Why doesn't the JVM cache JIT compiled code? I understand that JIT compilation is compilation to native code using hotspot mechanisms, which can be very very fast as it is optimization to the OS, Hardwards, etc. My question is, why does Java not store that JIT complied code somewhere in file and use the same for future purposes? This can reduce the 'initial warm-up' time as well. Please let me know what I am missing here. To add to my question: Why does not Java complie the complete code to native and use that always(for a specific JVM,OS, platform)? Why JIT? If I remember