February 2014 - core-libs-dev

ISO-8859-16 charset/decoder/encoder
by Charles Oliver Nutter 25 Aug '17

25 Aug '17

Again, apologies if this is not the right list. OpenJDK does not currently contain support for encoding/decoding ISO-8859-16. A JRuby user recently reported that they needed this encoding. So, I implemented it. Here's a link to the charset/decoder/encoder in JRuby's repository: https://github.com/jruby/jruby/blob/master/core/src/main/java/org/jruby/uti… I tried to follow the general form of the other ISO-8859 charsets, but there's probably more reuse possible (I believe they use sun.nio.cs classes I can't access). Happy to contribute this. Unsure of the process. - Charlie

4 5

Re: Analysis on JDK-8022321 java/lang/ref/OOMEInReferenceHandler.java fails intermittently
by David Holmes 24 Jun '16

24 Jun '16

Hi Kalyan, This is not a hotspot issue so I'm moving this to core-libs, please drop hotspot from any replies. On 20/12/2013 6:26 AM, srikalyan wrote: > Hi all, I have been working on the bug JDK-8022321 > <https://bugs.openjdk.java.net/browse/JDK-8022321> , this is a sporadic > failure and the webrev is available here > http://cr.openjdk.java.net/~srikchan/Regression/JDK-8022321_OOMEInReference… I'm really not sure what to make of this. We have a test that triggers an out-of-memory condition but the OOME can actually turn up in the ReferenceHandler thread causing it to terminate and the test to fail. We previously accounted for the non-obvious occurrences of OOME due to the Object.wait and the possible need to load the InterruptedException class - but still the OOME can appear where we don't want it. So finally you have just placed the whole for(;;) loop in a try/catch(OOME) that ignores the OOME. I'm certain that makes the test happy, but I'm not sure it is really what we want for the ReferenceHandler thread. If the OOME occurs while cleaning, or enqueuing then we will fail to clean and/or enqueue but there would be no indication that has occurred and I think that is a bigger problem than this test failing. There may be no way to make this test 100% reliable. In fact I'd suggest that no memory exhaustion test can be 100% reliable. David > * > **"Root Cause:Still not known"* > 2 places where there is a possibility for OOME > 1) Cleaner.clean() > 2) ReferenceQueue.enqueue() > > 1) The cleanup code in turn has 2 places where there is potential for > throwing OOME, > a) thunk Thread which is run from clean() method. This Runnable is > passed to Cleaner and appears in the following classes > java/nio/DirectByteBuffer.java > sun/misc/Perf.java > sun/nio/fs/NativeBuffer.java > sun/nio/ch/IOVecWrapper.java > sun/misc/Cleaner/ExitOnThrow.java > However none of the above overridden implementations ever create an > object in the clean() code. > b) new PrivilegedAction created in try catch Exception block of > clean() method but for this object to be created and to be held > responsible for OOME an Exception(other than OOME) has to be thrown. > > 2) No new heap objects are created in the enqueue method nor anywhere in > the deep call stack (VM.addFinalRefCount() etc) so this cannot be a > potential cause. > > *Experimental change to java.lang.Reference.java* : > - Put one more guard (try catch with OOME block) in the Reference > Handler Thread which may give the Reference Handler a chance to cleanup. > This is fixing the test failure (several 1000 runs with 0 failures) > - Without the above change the test fails atleast 3-5 times for every > 1000 run. > > *PS*: The code change is to a very critical part of JDK and i am fully > not aware of the consequences of the change, hence seeking expert help > here. Appreciate your time and inputs towards this. >

10 95

Replacement of Quicksort in java.util.Arrays with new Dual-Pivot Quicksort
by Vladimir Yaroslavskiy 27 Feb '16

27 Feb '16

Hello All, I'd like to share with you new Dual-Pivot Quicksort which is faster than the known implementations (theoretically and experimental). I'd like to propose to replace the JDK's Quicksort implementation by new one. Description ----------- The classical Quicksort algorithm uses the following scheme: 1. Pick an element P, called a pivot, from the array. 2. Reorder the array so that all elements, which are less than the pivot, come before the pivot and all elements greater than the pivot come after it (equal values can go either way). After this partitioning, the pivot element is in its final position. 3. Recursively sort the sub-array of lesser elements and the sub-array of greater elements. The invariant of classical Quicksort is: [ <= p | >= p ] There are several modifications of the schema: [ < p | = p | > p ] or [ = p | < p | > p | = p ] But all of them use *one* pivot. The new Dual-Pivot Quicksort uses *two* pivots elements in this manner: 1. Pick an elements P1, P2, called pivots from the array. 2. Assume that P1 <= P2, otherwise swap it. 3. Reorder the array into three parts: those less than the smaller pivot, those larger than the larger pivot, and in between are those elements between (or equal to) the two pivots. 4. Recursively sort the sub-arrays. The invariant of the Dual-Pivot Quicksort is: [ < P1 | P1 <= & <= P2 } > P2 ] The new Quicksort is faster than current implementation of Quicksort in JDK (L. Bentley and M. Douglas McIlroy) and classical Quicksort. The full description of the Dual-Pivot Quicksort you can find on my page: http://iaroslavski.narod.ru/quicksort Performance tests ----------------- Here is result of running on different types of input data: Client VM all 85% organ 0..1 0..4 random ascend descend equal equal pipes random 010101 random Dual-Pivot: 16.83 5.31 5.47 0.35 0.68 10.59 1.06 1.02 2.18 Bentley's: 19.77 9.08 10.13 0.63 1.12 13.22 1.63 1.08 2.49 Server VM all 85% organ 0..1 0..4 random ascend descend equal equal pipes random 010101 random Dual-Pivot: 23.94 6.68 6.63 0.43 0.62 17.14 1.42 1.96 3.41 Bentley's: 25.20 10.18 10.32 2.07 1.33 16.72 2.95 1.82 3.39 The a lot of other tests have been run under client and server mode. The most interesting is BentleyBasher test framework. It runs battery of tests for all cases: { 100, 1000, 10000, 1000000 } x { sawtooth, rand, stagger, plateau, shuffle } x { ident, reverse, reverse_front, reverse_back, sort, dither} where 100, ... , 1000000 - array length sawtooth: x[i] =i%m rand: x[i] = rand() % m stagger: x[i] = (i*m + i) % n plateau: x[i] = min(i, m) shuffle: x[i] = rand()%m? (j+=2): (k+=2) ident(x) - a copy of x reverse(x, 0, n) - reversed copy reverse_front(x, 0, n/2) - front half reversed reverse_back(x, n/2, n) - back half reversed sort(x) - an ordered copy dither(x) - add i%5 to x[i] Here is the result of execution: Server VM: http://spreadsheets.google.com/pub?key=t_EAWUkQ4mD3BIbOv8Fa-AQ&output=html Client VM: http://spreadsheets.google.com/pub?key=tdiMo8xleTxd23nKUObcz0Q&single=true&… Mathematical investigations --------------------------- It is proved that for the Dual-Pivot Quicksort the average number of comparisons is 2*n*ln(n), the average number of swaps is 0.8*n*ln(n), whereas classical Quicksort algorithm has 2*n*ln(n) and 1*n*ln(n) respectively. Full mathematical proof see in attached proof.txt and proof_add.txt files. Theoretical results are also confirmed by experimental counting of the operations. Diff between current and new implementation of Quicksort -------------------------------------------------------- Here is the link to the diff for java.util.Arrays class: http://cr.openjdk.java.net/~alanb/6880672/webrev.00 If you like to look and play with new algorithm, please, take attached class DualPivotQuicksort.java Feedback -------- Also I'd like to share a feedback from Joshua Bloch and Jon Bentley who spent a lot of time investigating this algorithm, who gave me many advices and tips how to make new Quicksort better. -------- Original Message -------- Subject: Re: Integration of new Dual-Pivot Quicksort into JDK 7 Date: Thu, 10 Sep 2009 07:20:11 -0700 From: Joshua Bloch <jjb(a)google.com> Jon also says that Vladimir should make every reasonable improvement to the basic method before checking in the code. In his words, "It would be horrible to put the new code into the library, and then have someone else come along and speed it up by another 20% by using standard techniques." I believe it's not unlikely that this code may end up getting ported to many languages and widely deployed in much the manner of Bentley and McIlroy's fine sort (which is nearing 20 successful years in the field). Jon will help Vladimir do this. -------- Original Message -------- Subject: Dual-Pivot Quicksort: Next Steps Date: Wed, 09 Sep 2009 15:02:25 -0400 From: Jon Bentley <jbentley(a)avaya.com> Vladimir, Josh, I *finally* feel like I understand what is going on. Now that I (think that) I see it, it seems straightforward and obvious. Tony Hoare developed Quicksort in the early 1960s. I was very proud to make minor contributions to a particularly clean (binary) quicksort in the mid 1980s, to a relatively straightforward, industrial strength Quicksort with McIlroy in the early 1990s, and then to algorithms and data structures for strings with Sedgewick in the mid 1990s. I think that Vladimir's contributions to Quicksort go way beyond anything that I've ever done, and rank up there with Hoare's original design and Sedgewick's analysis. I feel so privileged to play a very, very minor role in helping Vladimir with the most excellent work! ----------------------------------------------- Let me know, if you have any questions/comments. Thank you, Vladimir Here is the details between steps (1) and (2) in the case of Dual-Pivot Quicksort: ----------------------------------------------------------------------------- >From the algorithm above, the average number of comparisons C_n as a function of the number of elements may be represented by the equation: (1) C_n = 1 + 2/(n*(n-1)) * sum_{i=0}^{n-2} sum_{j=i+1}^{n-1} {C_i + 1*i + C_{j-i-1} + 2*(j-i- 1) + C_{n-j-1} + 2*(n-j-1)} Equation (1) means that total number is the sum of the comparison numbers of all cases of partitions into 3 parts plus number of comparisons for elements from left part (one comparison), center and right parts (2 comparisons). !!! It can be rewritten in other way: !!! (2) C_n = 1 + R*(n-2) + 2/(n(n-1)) * sum_{i=0}^{n-2} sum_{j=i+1}^{n-1} {C_i + C_{j-i-1} + C_{n-j-1}} where R is the average number of comparisons during one iteration. This constant R can be found as average value (1 + 2 + 2) / 3 = 5/3 ~ 1.6666. ----------------------------------------------------------------------------- How it can be rewritten: We should show that 2/(n*(n-1)) * sum_{i=0}^{n-2} sum_{j=i+1}^{n-1} {i + 2*(j-i- 1) + 2*(n-j-1)} equals to 5/3 * (n-2). Let's consider the double sum: sum_{i=0}^{n-2} sum_{j=i+1}^{n-1} {i + 2*(j-i- 1) + 2*(n-j-1)} = = sum_{i=0}^{n-2} sum_{j=i+1}^{n-1} {2*n - 4} - sum_{i=0}^{n-2} sum_{j=i+1}^{n-1} {i} = = 2*(n-2)*sum_{i=0}^{n-2} {n-1-i} - sum_{i=0}^{n-2} sum_{j=i+1}^{n-1} {i} = = 2*(n-2)*((n-1)^2 - (n-1)*(n-2)/2) - (n-1)*sum_{i=0}^{n-2} {i} + sum_{i=0}^{n-2} {i*i} = ------------------------------------------------------------------ here we use the property: sum_{k=1}{n} {k^2} = n^3/3 + n^2/2 + n/6 ------------------------------------------------------------------ = 2*(n-2)*((n-1)^2 - (n-1)*(n-2)/2) - (n-1)*(n-1)*(n-2)/2 + (n-2)^3/3 + (n-2)^2/2 + (n-2)/6 = = 1/6 * (12*(n-1)^2*(n-2) - 6*(n-1)*(n-2)^2 - 3*(n-1)^2*(n-2) + 2*(n-2)^3 + 3*(n-2)^2 + (n-2)) = = 1/6 * (3*(n-1)*(n-2)*(3*(n-1) - 2*(n-2)) + (n-2)*(2*(n-2)^2 + 3*(n-2) + 1))) = = 1/6 * (3*(n-1)*(n-2)*(n+1) + (n-2)*(2*n^2 - 5*n + 3)) = = 1/6 * (n-2)*(5*n^2 - 5*n) = 5/6 * n*(n-1)*(n-2) Substitute the result into equation (1): (1.1) C_n = 1 + 2/(n*(n-1)) * 5/6 * n*(n-1)*(n-2) + 2/(n*(n-1)) * sum_{i=0}^{n-2} sum_{j=i+1}^{n-1} {C_i + C_{j-i-1} + C_{n-j-1}} or (1.2) C_n = 1 + 5/3*(n-2) + 2/(n*(n-1)) * sum_{i=0}^{n-2} sum_{j=i+1}^{n-1} {C_i + C_{j-i-1} + C_{n-j-1}} We see that (1.2) is same as (2). /** * @author Vladimir Yaroslavskiy * @version 2009.09.10 m765 */ public class DualPivotQuicksort { public static void sort(int[] a) { sort(a, 0, a.length); } public static void sort(int[] a, int fromIndex, int toIndex) { rangeCheck(a.length, fromIndex, toIndex); dualPivotQuicksort(a, fromIndex, toIndex - 1, 3); } private static void rangeCheck(int length, int fromIndex, int toIndex) { if (fromIndex > toIndex) { throw new IllegalArgumentException("fromIndex(" + fromIndex + ") > toIndex(" + toIndex + ")"); } if (fromIndex < 0) { throw new ArrayIndexOutOfBoundsException(fromIndex); } if (toIndex > length) { throw new ArrayIndexOutOfBoundsException(toIndex); } } private static void swap(int[] a, int i, int j) { int temp = a[i]; a[i] = a[j]; a[j] = temp; } private static void dualPivotQuicksort(int[] a, int left, int right, int div) { int len = right - left; if (len < 27) { // insertion sort for tiny array for (int i = left + 1; i <= right; i++) { for (int j = i; j > left && a[j] < a[j - 1]; j--) { swap(a, j, j - 1); } } return; } int third = len / div; // "medians" int m1 = left + third; int m2 = right - third; if (m1 <= left) { m1 = left + 1; } if (m2 >= right) { m2 = right - 1; } if (a[m1] < a[m2]) { swap(a, m1, left); swap(a, m2, right); } else { swap(a, m1, right); swap(a, m2, left); } // pivots int pivot1 = a[left]; int pivot2 = a[right]; // pointers int less = left + 1; int great = right - 1; // sorting for (int k = less; k <= great; k++) { if (a[k] < pivot1) { swap(a, k, less++); } else if (a[k] > pivot2) { while (k < great && a[great] > pivot2) { great--; } swap(a, k, great--); if (a[k] < pivot1) { swap(a, k, less++); } } } // swaps int dist = great - less; if (dist < 13) { div++; } swap(a, less - 1, left); swap(a, great + 1, right); // subarrays dualPivotQuicksort(a, left, less - 2, div); dualPivotQuicksort(a, great + 2, right, div); // equal elements if (dist > len - 13 && pivot1 != pivot2) { for (int k = less; k <= great; k++) { if (a[k] == pivot1) { swap(a, k, less++); } else if (a[k] == pivot2) { swap(a, k, great--); if (a[k] == pivot1) { swap(a, k, less++); } } } } // subarray if (pivot1 < pivot2) { dualPivotQuicksort(a, less, great, div); } } } At first consider the classic Quicksort scheme and find the average number of comparisons and swaps for it. We assume that input data is random permutation of n numbers from the range [1..n]. Classic Quicksort ================= 1. Choose a pivot element (take random), 2. Compare each (n-1) elements with the pivot 3. and swap it, if necessary, to have the partitions: [ <= pivot | >= pivot ] 4. Sort recursively left and right parts. >From the algorithm above, the average number of comparisons C_n as a function of the number of elements may be represented by the equation: (1) C_n = (n-1) + 1/n * sum_{k=0}^{n-1} {C_k + C_{n-k-1}} and last sum can be rewritten: (2) C_n = (n-1) + 2/n * sum_{k=0}^{n-1} C_k Write formula (2) for n+1: (3) C_{n+1} = n + 2/(n+1) * sum_{k=0}^{n} C_k Multiply (2) by n and (3) by (n+1) and subtract one from other, we have: (4) (n+1)*C_{n+1} - n*C_n = 2*n + 2*C_n Sorting an array of n elements may be considered as selecting one permutation of the n elements among all possible permutations. The number of possible permutations of n elements is n!, so the task for any sorting algorithm is to determine the one permutation out of n! possibilities. The minimum number of operations (swap and comparisons) for sorting n elements is const*ln(n!). >From the Stirling's formula the approximation of the number of operations is A*n*ln(n) + B*n + C, where A, B and C are constant coefficients. The coefficients B and C are not important for large n. Therefore, the function C_n may be approximated by the equation: (5) C_n = A*n*ln(n) The function C_n is substituted from equation (5) into equation (4), which yields the following equation: (6) (n+1)*A*(n+1)*ln(n+1) - n*A*n*ln(n) = 2*n + 2*A*n*ln(n) Using the properties of logarithms, equation (6) can then be reduced to: (7) n*ln(1+1/n) + 2*ln(1+1/n) + (1/n) * ln(n+1) = 2/A Using a property of logarithm: ln(1 + x) -> x, if x -> 0, and other property: ln(n) / n -> 0, when n -> +oo, equation (7) will be approximated by: (8) 1 + 0 + 0 = 2/A So, the coefficient A is equal to 2 and the average number of comparisons in sorting of n size arrays is (9) C_n = 2*n*ln(n). To find the approximation of the average number of swaps, we use the similar approach as in the case of comparisons. The average number of swaps S_n as a function of the number of elements may be represented by the equation: (10) S_n = 1/2*(n-1) + 2/n * sum_{k=0}^{n-1} S_k We assume that average number of swaps during one iteration is 1/2*(n-1). It means that in average one half of elements is swapped only. Using the same approach, we find that the coefficient A equals to 1. Therefore, the function S_n may be approximated by the equation: (11) S_n = n*ln(n) ------------------------------------------------------------------------------- Now consider the Dual-Pivot Quicksort scheme and find the average number of comparisons and swaps for it. We assume that input data is random permutation of n numbers from the range [1..n]. Dual-Pivot Quicksort ==================== 1. Choose 2 pivot elements pivot1 and pivot2 (take random), 2. pivot1 must be less or equal than pivot2, otherwise they are swapped 3. Compare each (n-2) elements with the pivots 4. and swap it, if necessary, to have the partitions: [ <= p1 | p1 <= & <= p2 | >= p2 ] 5. Sort recursively left, center and right parts. >From the algorithm above, the average number of comparisons C_n as a function of the number of elements may be represented by the equation: (1) C_n = 1 + 2/(n*(n-1)) * sum_{i=0}^{n-2} sum_{j=i+1}^{n-1} {C_i + 1*i + C_{j-i-1} + 2*(j-i-1) + C_{n-j-1} + 2*(n-j-1)} Equation (1) means that total number is the sum of the comparison numbers of all cases of partitions into 3 parts plus number of comparisons for elements from left part (one comparison), center and right parts (2 comparisons). It can be rewritten in other way: (2) C_n = 1 + R*(n-2) + 2/(n(n-1)) * sum_{i=0}^{n-2} sum_{j=i+1}^{n-1} {C_i + C_{j-i-1} + C_{n-j-1}} where R is the average number of comparisons during one iteration. This constant R can be found as average value (1 + 2 + 2) / 3 = 5/3, which means that elements from left part are required only one comparison, and elements form center and right parts - two comparisons. The double sum in equation (2) can be reduced: (3) C_n = 1 + 5/3*(n-2) + 2/(n*(n-1)) * sum_{k=0}^{n-2} {3 * (n-k-1) * C_k} Denote 1 + 5/3*(n-2) by f_n and multiply to n*(n-1): (4) n*(n-1)*C_n = n*(n-1)*f_n + 6 * sum_{k=0}^{n-2} {(n-k-1) * C_k} Write formula (4) for n+1: (5) n*(n+1)*C_{n+1} = n*(n+1)*f_n + 6 * sum_{k=0}^{n-1} {(n-k) * C_k} Subtract (4) from (5), we have: (6) n*(n+1)*C_{n+1} - n*(n-1)*C_n = n*(n+1)*f_n - n*(n-1)*f_n + 6 * sum_{k=0}^{n-2} C_k + 6*C_{n-1} Denote n*(n+1)*C_{n+1} - n*(n-1)*C_n by X_n and n*(n+1)*f_n - n*(n-1)*f_n by F_n: (7) X_n = F_n + 6 * sum_{k=0}^{n-2} C_k + 6*C_{n-1} Write formula (7) for n+1: (8) X_{n+1} = F_{n+1} + 6 * sum_{k=0}^{n-1} C_k + 6*C_n Subtract (7) from (8), we have: (9) X_{n+1} - X_n = F_{n+1} - F_n + 6*C_n Resolving of F_{n+1} - F_n gives: (10) X_{n+1} - X_n = 2 + 10*n + 6*C_n The function X_n is substituted into equation (10), which yields the following equation: (11) (n+1)*(n+2)C_{n+2} -2*n(n+1)*C_{n+1} + (n*(n-1) - 6)*C_n = 2 + 10*n We will find the function C_n approximated by the equation: (12) C_n = A*n*ln(n) The function C_n is substituted from equation (12) into equation (11), which yields the following equation: (13) (n^3+5*n^2+8*n+4)*ln(n+2) - (2*n^3+4*n^2+2*n)*ln(n+1) + (n^3-n^2-6*n)*ln(n) = (10*n+2) / A Using the properties of logarithms, equation (13) can then be reduced to: (14) n^3*(ln(n+2)-2*ln(n+1)+ln(n)) + n^2*(5*ln(n+2)-4*ln(n+1)-ln(n)) + n*(8*ln(n+2)-2*ln(n+1)-6*ln(n)) + 4*ln(n+2) = (10*n+2) / A Using a property of logarithm: ln(1 + x) -> x, if x -> 0, and other property: ln(n) / n -> 0, when n -> +oo, equation (15) will be approximated by: (15) -1 + 4 + 2 + 0 + 0 = 10 / A So, the coefficient A is equal to 2 and the average number of comparisons in sorting of n size arrays is (9) C_n = 2*n*ln(n). To find the approximation of the average number of swaps, we use the similar approach as in the case of comparisons. The average number of swaps S_n as a function of the number of elements may be represented by the equation: (10) S_n = 4 + 2/3*(n-2) + 2/(n*(n-1)) * sum_{k=0}^{n-2} {(n-k-1)*S_k} We assume that average number of swaps during one iteration is 2/3*(n-2). It means that in average one third of elements is swapped only. Using the same approach, we find that the coefficient A equals to 0.8. Therefore, the function S_n may be approximated by the equation: (11) S_n = 0.8*n*ln(n) ------------------------------------------------------------------------- And as summary: The value of the coefficient A: dual-pivot classic comparison: 2.0 2.0 swap: 0.8 1.0

14 20

JEP 187: Serialization 2.0
by mark.reinhold＠oracle.com 30 Dec '14

30 Dec '14

Posted: http://openjdk.java.net/jeps/187 - Mark

5 8

RFR: Faster ZipFile.getEntry()/entries()
by Xueming Shen 22 May '14

22 May '14

Hi, The change proposed here is to bring the zip entry handing code from the native level to the java level. This effectively solves the performance issues of ZipFile.getEntry and entries() that is caused by multiple jni invocation steps to generate one single ZipEntry (see ZipFile.getZipEntry()). A simple non-scientific benchmark test of simply iterating the ZipFile via the Enumeration from entries() on rt.jar/charsets.jar suggests a 50%+ speed boost. http://cr.openjdk.java.net/~sherman/zipfile_j/webrev Couple notes: (1) Ideally it might be desired to go further to bring all the native code of ZipFile to java level (which should help completely remove that mmap crash issue, have better file and memory management... ), but it is suggested that it might be better to limit the scope of the change at this late release circle. (2) JavaFile.read0() is the version that uses "getPrimitiveArrayCritical" to read file bits into the java array directly (instead of using a stack buffer and then copy into the java array), which appears to be 5% faster. But I can't make up my mind of which one would be better. Given (1) the trouble we had before in De/Infalter code (when the getPrimitiveArrayCritical is being heavily used), (2) FileInputStream uses the same "copy" approach, I'm staying with the "copy" appraoch, but option appreciated. (3) We will have to keep the native implementation (zip_util.c) for the vm directly access. Thanks! -Sherman

3 4

RFR 9: 8034903: Add Logging of Process.start arguments and resulting pid
by roger riggs 15 May '14

15 May '14

Hi, Having folks stumbling over process creation and problems of quoting, especially on windows, it seems useful to log the native commands and arguments. They are proposed to be logged using the PlatformLogger at Level.FINE which will not be logged by default. The environment is useful in some cases, but verbose, that it should be Logged at FINER. The pid of the spawned process logged as well for traceability. Please take a look at this first draft and comment on whether it is useful, a good idea and any improvements needed. Webrev: http://cr.openjdk.java.net/~rriggs/webrev-log-processcreate/ Thanks, Roger

13 27

RFR (S) 8025238: nsk/jvmti/scenarios/bcinstr/BI04/bi04t002 crashed with SIGSEGV
by Coleen Phillimore 02 May '14

02 May '14

Summary: Redefined class in stack trace may not be found by method_idnum so handle null. This is a simple change. I had another change to save the method name (as u2) in the backtrace, but it's not worth the extra footprint in backtraces for this rare case. The root problem was that we save method_idnum in the backtrace (u2) instead of Method* to avoid Method* from being redefined and deallocated. I made a change to InstanceKlass::method_from_idnum() to return null rather than the last method in the list, which causes this crash. Dan and I went down the long rabbit-hole of why method_idnum is changed for obsolete methods and we think there's some cleanup and potential bugs in this area. But this is not that change. I'll file another bug to continue this investigation for jdk9 (or 8uN). Staffan created a test - am including core-libs for the review request. Also tested with all of the vm testbase tests, mlvm tests, and java/lang/instrument tests. open webrev at http://cr.openjdk.java.net/~coleenp/8025238/ bug link https://bugs.openjdk.java.net/browse/JDK-8025238 test case for jdk8 repository: open webrev at http://cr.openjdk.java.net/~coleenp/8025238_jdk Thanks, Coleen

6 15

RFR: 8035584: (s) ArrayList(c) should avoid inflation if c is empty
by Mike Duigou 25 Apr '14

25 Apr '14

Hello all; This changeset consists of two small performance improvements for ArrayList. Both are related to the lazy initialization introduced in JDK-8011200. The first change is in the ArrayList(int capacity) constructor and forces lazy initialization if the requested capacity is zero. It's been observed that in cases where zero capacity is requested that it is very likely that the list never receives any elements. For these cases we permanently avoid the allocation of an element array. The second change, noticed by Gustav Åkesson, involves the ArrayList(Collection c) constructor. If c is an empty collection then there is no reason to inflate the backing array for the ArrayList. http://cr.openjdk.java.net/~mduigou/JDK-8035584/0/webrev/ I also took the opportunity to the <tt></tt> -> {@code } conversion for the javadoc. Enjoy! Mike

4 18

JDK-8036003: Add variable not to separate debug information.
by Yasumasa Suenaga 23 Apr '14

23 Apr '14

Hi all, Currently, configure script can accept --disable-debug-symbols and --disable-zip-debug-info as controlling to generate debug information. However, current makefiles cannot build ELF binaries which is contained debug information with "images" target. Some Linux distros use RPM as package manager. RPM is built by rpmbuild command, it strips symbols and debug information during to generate rpm packages. https://fedoraproject.org/wiki/Packaging:Debuginfo For example, OpenJDK8 in Fedora20 ships libjvm.so and libjvm.debuginfo . libjvm.debuginfo is generated in OpenJDK's makefiles, however it does not contain debug information. Actual debug information is shipped by OpenJDK debuginfo package. This packaging is important when we have to debug JVM/JNI libraries. If we want to use debugging tools (GDB, SystemTap, etc...), they may requires debuginfo package. Debuggee (e.g. libjvm.so) points libjvm.so.debug which is located at sub directory in /usr/lib/debug . It is defined in ELF section (.note.gnu.build-id) of libjvm.so . However libjvm.so.debug does not contain valid debug information. We need to access libjvm.debuginfo.debug at same location. When we think to build OpenJDK rpm packages, we have to build ELF binaries which contain all debug information. we should not to separate debug information. Thus I want to add a variable "SEPARATED_DEBUGINFO_FILES" . If we pass "SEPARATED_DEBUGINFO_FILES=false" to make command, "make" does not execute objcopy command for generating debuginfo binaries. If we build rpm packages, we also have to add "STRIP_POLICY=no_strip" to arguments of make command. Or ELF binaries will be stripped. I've uploaded webrev for this enhancement. This is separated 3-part: top of forest, hotspot, jdk http://cr.openjdk.java.net/~ysuenaga/JDK-8036003/ Please review it and sponsoring! Thanks, Yasumasa P.S. I tried to add SEPARATED_DEBUGINFO_FILES as a configure option like "--disable-separated-debug-info" . I ran configure which is located at jdk9/dev directory after I ran autoconf and common/autoconf/autogen.sh. However it failed. I guess this is caused by changeset as below. JDK-8035495: Improvements in autoconf integration http://hg.openjdk.java.net/jdk9/dev/rev/6e29cd9ac2b4 This changes add "CHECKME" option to configure script. However, this changes affects "configure" only. It should change "configure.ac" .

10 41

A hole in the serialization spec
by David M. Lloyd 17 Apr '14

17 Apr '14

Since the topic of serialization has come up recently, I'll take it as an excuse to bring up a problem that I've run into a couple of times with the serialization specification, which has resulted in user problems. If you read section 2.3 [1] of the specification, it says: "The class's writeObject method, if implemented, is responsible for saving the state of the class. Either ObjectOutputStream's defaultWriteObject or writeFields method must be called once (and only once) before writing any optional data that will be needed by the corresponding readObject method to restore the state of the object; even if no optional data is written, defaultWriteObject or writeFields must still be invoked once. If defaultWriteObject or writeFields is not invoked once prior to the writing of optional data (if any), then the behavior of instance deserialization is undefined in cases where the ObjectInputStream cannot resolve the class which defined the writeObject method in question." If you go to section 3.4 [2] of the specification, it reads: "The readObject method of the class, if implemented, is responsible for restoring the state of the class. The values of every field of the object whether transient or not, static or not are set to the default value for the fields type. Either ObjectInputStream's defaultReadObject or readFields method must be called once (and only once) before reading any optional data written by the corresponding writeObject method; even if no optional data is read, defaultReadObject or readFields must still be invoked once." Now the problem: there are many classes in the wild which nevertheless do not write/read fields. We cause an exception in such cases rather than make up some undefined behavior. What I'm wondering is, is there some sensible behavior that could be specified for this case? The Oracle JDK seems to simply leave fields uninitialized in this case, maybe that can be a specified behavior? [1] http://docs.oracle.com/javase/7/docs/platform/serialization/spec/output.htm… [2] http://docs.oracle.com/javase/7/docs/platform/serialization/spec/input.html… -- - DML

6 22