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Tech Tools Tech Tools
NEWS Tech Tools Tech Tools LLVM 3.0 Released Low Level Virtual Machine (LLVM) recently announced ver- replacing the C/ Objective-C compiler in the GCC system with a sion 3.0 of it compiler infrastructure. Originally implemented more easily integrated system and wider support for multithread- for C/ C++, the language-agnostic design of LLVM has ing. Other features include a new register allocator (which can spawned a wide variety of provide substantial perfor- front ends, including Objec- mance improvements in gen- tive-C, Fortran, Ada, Haskell, erated code), full support for Java bytecode, Python, atomic operations and the Ruby, ActionScript, GLSL, new C++ memory model, Clang, and others. and major improvement in The new release of LLVM the MIPS back end. represents six months of de- All LLVM releases are velopment over the previous available for immediate version and includes several download from the LLVM re- major changes, including dis- leases web site at: http:// continued support for llvm- llvm.org/releases/. For more gcc; the developers recom- information about LLVM, mend switching to Clang or visit the main LLVM website DragonEgg. Clang is aimed at at: http://llvm.org/. YaCy Search Engine Online Open64 5.0 Released The YaCy project has released version 1.0 of its Open64, the open source (GPLv2-licensed) peer-to-peer Free Software search engine. YaCy compiler for C/C++ and Fortran that’s does not use a central server; instead, its search re- backed by AMD and has been developed by sults come from a network of independent peers. SGI, HP, and various universities and re- According to the announcement, in this type of dis- search organizations, recently released ver- tributed network, no single entity decides which sion 5.0. -
It's Always Sunny with Openj9
It’s always sunny with OpenJ9 Dan Heidinga, Eclipse OpenJ9 Project Lead VM Architect, IBM Runtimes @danheidinga DanHeidinga 2 https://upload.wikimedia.org/wikipedia/commons/9/98/Storm_clouds.jpg cjohnson7 from Rochester, Minnesota / CC BY (https://creativecommons.org/licenses/by/2.0) My Day Job http://docs.oracle.com/javase/8/docs/index.html Eclipse OpenJ9 Created Sept 2017 http://www.eclipse.org/openj9 https://github.com/eclipse/openj9 Dual License: Eclipse Public License v2.0 Apache 2.0 Users and contributors very welcome https://github.com/eclipse/openj9/blob/master/CO NTRIBUTING.md 6 A JVM for the cloud 7 Built with the Class Libraries you know and love JDK JDK JDK JDK 8 11 14 next Single source stream at OpenJ9 No (LTS) JDK left behind! 8 Right, the cloud 9 Cloud requirements on Java ▪ Fast startup –Faster scaling for increased demand ▪ Small footprint –Improves density on servers –Improves cost for applications ▪ Quick / immediate rampup –GB/hr is key, if you run for less time you pay less money 10 OpenJ9 helps… … containers out of the box 11 Automatically detect if running in a container ▪ Based on the container limits: – Tune the GC Heap – Limit GC & active JIT threads – Constrain Runtime.availableProcessors() to cgroup quotas – Out of the box idle tuning 12 Avoid rebuilding containers just to adjust heap size ▪ -XX:InitialRAMPercentage=N – Set initial heap size as a percentage of total memory ▪ -XX:MaxRAMPercentage=N – Set maximum heap size as a percentage of total memory ▪ Running in containers with set memory limits? – OpenJ9 -
Oracle Utilities Testing Accelerator Licensing Information User Manual Release 6.0.0.3.0 F35952-01
Oracle Utilities Testing Accelerator Licensing Information User Manual Release 6.0.0.3.0 F35952-01 June 2021 Oracle Utilities Testing Accelerator Licensing Information User Manual, Release 6.0.0.3.0 Copyright © 2019, 2021 Oracle and/or its affiliates. All rights reserved. This software and related documentation are provided under a license agreement containing restrictions on use and disclosure and are protected by intellectual property laws. Except as expressly permitted in your license agreement or allowed by law, you may not use, copy, reproduce, translate, broadcast, modify, license, transmit, distribute, exhibit, perform, publish, or display any part, in any form, or by any means. Reverse engineering, disassembly, or decompilation of this software, unless required by law for interoperability, is prohibited. The information contained herein is subject to change without notice and is not warranted to be error-free. If you find any errors, please report them to us in writing. If this is software or related documentation that is delivered to the U.S. Government or anyone licensing it on behalf of the U.S. Government, then the following notice is applicable: U.S. GOVERNMENT END USERS: Oracle programs (including any operating system, integrated software, any programs embedded, installed or activated on delivered hardware, and modifications of such programs) and Oracle computer documentation or other Oracle data delivered to or accessed by U.S. Government end users are "commercial computer software" or "commercial computer software documentation" -
Porting Open64 to the Cygwin Environment
Porting Open64 to the Cygwin Environment Nathan Tallent Mike Fagan ∗ November 2003 Abstract Cygwin is a Linux-like environment for Windows. It is sufficiently com- plete and stable that porting even very large codes to the environment is relatively straightforward. Cygwin is easy enough to download and install that it provides a convenient platform for traveling with code and giving conference demonstra- tions (via laptop) – even potentially expanding a code’s audience and user base (via desktop). However, for various reasons, Cygwin is not 100% compatible with Linux. We describe the major problems we encountered porting Rice University’s Open64/SL code base to Cygwin and present our solutions. 1 Introduction Cygwin is a Linux-like environment for Windows. It provides a Windows DLL that emulates nearly all of the Linux API. Programs written for Linux can link with the Cygwin DLL and thus run on Windows. A reasonably proficient Unix user can easily download and install a relatively complete Linux environment, including shells, de- velopment tools (GCC, make, gdb), editors (emacs, vi) and a graphical environment (XFree86). Moreover, the environment is complete and stable enough that it is rela- tively straightforward to port even very large codes to Cygwin. Because of the preva- lence of Windows-based desktops and laptops, Cygwin provides an attractive means for traveling with code and demonstrating it at conferences – even potentially expand- ing a code’s audience and user base. However, for full-time development or for critical applications, Unix remains superior. More information about Cygwin can be found on its web site, http://www.cygwin.com. -
Current State of EA and Its Uses in The
Jfokus 2020 Current state of EA and Charlie Gracie Java Engineering Group at Microsoft its uses in the JVM Overview • Escape Analysis and Consuming Optimizations • Current State of Escape Analysis in JVM JITs • Escape Analysis and Allocation Elimination in Practice • Stack allocation 2 Escape Analysis and Consuming Optimizations 3 What is Escape Analysis? • Escape Analysis is a method for determining the dynamic scope of objects -- where in the program an object can be accessed. • Escape Analysis determines all the places where an object can be stored and whether the lifetime of the object can be proven to be restricted only to the current method and/or thread. 4 https://en.wikipedia.org/wiki/Escape_analysis Partial Escape Analysis • A variant of Escape Analysis which tracks object lifetime along different control flow paths of a method. • An object can be marked as not escaping along one path even though it escapes along a different path. 5 https://en.wikipedia.org/wiki/Escape_analysis EA Consuming Optimizations 1. Monitor elision • If an object does not escape the current method or thread, then operations can be performed on this object without synchronization 2. Stack allocation • If an object does not escape the current method, it may be allocated in stack memory instead of heap memory 3. Scalar replacement • Improvement to (2) by breaking an object up into its scalar parts which are just stored as locals 6 Current State of Escape Analysis in JVM JITs 7 HotSpot C2 EA and optimizations • Flow-insensitive1 implementation based on the -
Automated Malware Analysis Report for Funny Linux.Elf
ID: 449051 Sample Name: funny_linux.elf Cookbook: defaultlinuxfilecookbook.jbs Time: 05:20:57 Date: 15/07/2021 Version: 33.0.0 White Diamond Table of Contents Table of Contents 2 Linux Analysis Report funny_linux.elf 3 Overview 3 General Information 3 Detection 3 Signatures 3 Classification 3 Analysis Advice 3 General Information 3 Process Tree 3 Yara Overview 3 Jbx Signature Overview 4 Mitre Att&ck Matrix 4 Malware Configuration 4 Behavior Graph 4 Antivirus, Machine Learning and Genetic Malware Detection 5 Initial Sample 5 Dropped Files 5 Domains 5 URLs 5 Domains and IPs 5 Contacted Domains 5 Contacted IPs 5 Runtime Messages 6 Joe Sandbox View / Context 6 IPs 6 Domains 6 ASN 6 JA3 Fingerprints 6 Dropped Files 6 Created / dropped Files 6 Static File Info 6 General 6 Static ELF Info 7 ELF header 7 Sections 7 Program Segments 8 Dynamic Tags 8 Symbols 8 Network Behavior 10 System Behavior 10 Analysis Process: funny_linux.elf PID: 4576 Parent PID: 4498 10 General 10 File Activities 10 File Read 10 Copyright Joe Security LLC 2021 Page 2 of 10 Linux Analysis Report funny_linux.elf Overview General Information Detection Signatures Classification Sample funny_linux.elf Name: SSaampplllee hhaass sstttrrriiippppeedd ssyymbboolll tttaabblllee Analysis ID: 449051 Sample has stripped symbol table MD5: e0ba4089e9b457… Ransomware SHA1: 21b3392a2fdab2a… Miner Spreading SHA256: d2544462756205… mmaallliiiccciiioouusss malicious Evader Phishing Infos: sssuusssppiiiccciiioouusss suspicious cccllleeaann clean Exploiter Banker Spyware Trojan / Bot Adware Score: -
CPU Function (Defining Location) CPU Time in Seconds
NASA Open|SpeedShop Tutorial Performance Analysis with Open|SpeedShop Contributors: Jim Galarowicz, Bill Hachfeld, Greg Schultz: Argo Navis Technologies Don Maghrak, Patrick Romero: Krell Institute Martin Schulz, Matt Legendre: Lawrence Livermore National Laboratory Jennifer Green, Dave Montoya: Los Alamos National Laboratory Mahesh Rajan, Doug Pase: Sandia National Laboratories Koushik Ghosh, Engility Dyninst group (Bart Miller: UW & Jeff Hollingsworth: UMD) Phil Roth, Mike Brim: ORNL LLNL-PRES-503451 Dec 12, 2016 Outline Introduction to Open|SpeedShop How to run basic timing experiments and what they can do? How to deal with parallelism (MPI and threads)? How to properly use hardware counters? Slightly more advanced targets for analysis How to understand and optimize I/O activity? How to evaluate memory efficiency? How to analyze codes running on GPUs? DIY and Conclusions: DIY and Future trends Hands-on Exercises (after each section) On site cluster available We will provide exercises and test codes Performance Analysis With Open|SpeedShop: NASA Hands-On Tutorial Dec 12, 2016 2 NASA Open|SpeedShop Tutorial Performance Analysis with Open|SpeedShop Section 1 Introduction to Open|SpeedShop Dec 12, 2016 Open|SpeedShop Tool Set Open Source Performance Analysis Tool Framework Most common performance analysis steps all in one tool Combines tracing and sampling techniques Gathers and displays several types of performance information Flexible and Easy to use User access through: GUI, Command Line, Python Scripting, convenience -
2010-2011 Annual Report
Software in the Public Interest, Inc. 2010-2011 Annual Report 1st July 2011 To the membership, board and friends of Software in the Public Interest, Inc: As mandated by Article 8 of the SPI Bylaws, I respectfully submit this annual report on the activities of Software in the Public Interest, Inc. and extend my thanks to all of those who contributed to the mission of SPI in the past year. { Jonathan McDowell, SPI Secretary 1 Contents 1 President's Welcome3 2 Committee Reports4 2.1 Membership Committee.......................4 2.1.1 Statistics...........................4 3 Board Report5 3.1 Board Members............................5 3.2 Board Changes............................6 3.3 Elections................................6 4 Treasurer's Report7 4.1 Income Statement..........................7 4.2 Balance Sheet.............................9 5 Member Project Reports 11 5.1 New Associated Projects....................... 11 5.1.1 LibreOffice.......................... 11 5.1.2 Open64............................ 11 5.1.3 Jenkins............................ 11 5.1.4 ankur.org.in.......................... 12 A About SPI 13 2 Chapter 1 President's Welcome In the time that has passed since I originally agreed to serve as President sev- eral years ago, SPI has made significant and enduring progress resolving long- standing procedural problems that once impeded our ability to meet the basic service expectations of our associated projects. These accomplishments are of course not my own, but the collective result of work contributed by a dedicated core group of volunteers including the rest of our officers and board. A measure of our success is the steady stream of new SPI associated projects in recent years, including those we invited to join us in the last year listed later in this report. -
Proceedings of the 7 Python in Science Conference
Proceedings of the 7th Python in Science Conference SciPy Conference – Pasadena, CA, August 19-24, 2008. Editors: Gaël Varoquaux, Travis Vaught, Jarrod Millman Proceedings of the 7th Python in Science Conference (SciPy 2008) Contents Editorial 3 G. Varoquaux, T. Vaught, J. Millman The State of SciPy 5 J. Millman, T. Vaught Exploring Network Structure, Dynamics, and Function using NetworkX 11 A. Hagberg, D. Schult, P. Swart Interval Arithmetic: Python Implementation and Applications 16 S. Taschini Experiences Using SciPy for Computer Vision Research 22 D. Eads, E. Rosten The SciPy Documentation Project (Technical Overview) 27 S. Van der Walt Matplotlib Solves the Riddle of the Sphinx 29 M. Droettboom The SciPy Documentation Project 33 J. Harrington Pysynphot: A Python Re-Implementation of a Legacy App in Astronomy 36 V. Laidler, P. Greenfield, I. Busko, R. Jedrzejewski How the Large Synoptic Survey Telescope (LSST) is using Python 39 R. Lupton Realtime Astronomical Time-series Classification and Broadcast Pipeline 42 D. Starr, J. Bloom, J. Brewer Analysis and Visualization of Multi-Scale Astrophysical Simulations Using Python and NumPy 46 M. Turk Mayavi: Making 3D Data Visualization Reusable 51 P. Ramachandran, G. Varoquaux Finite Element Modeling of Contact and Impact Problems Using Python 57 R. Krauss Circuitscape: A Tool for Landscape Ecology 62 V. Shah, B. McRae Summarizing Complexity in High Dimensional Spaces 66 K. Young Converting Python Functions to Dynamically Compiled C 70 I. Schnell 1 unPython: Converting Python Numerical Programs into C 73 R. Garg, J. Amaral The content of the articles of the Proceedings of the Python in Science Conference is copyrighted and owned by their original authors. -
The CUDA Compiler Driver NVCC
The CUDA Compiler Driver NVCC Last modified on: <04-01-2008> Document Change History Version Date Responsible Reason for Change beta 01-15-2007 Juul VanderSpek Initial release 0.1 05-25-2007 Juul VanderSpek CUDA 0.1 release 1.0 06-13-2007 Juul VanderSpek CUDA 1.0 release 1.1 10-12-2007 Juul VanderSpek CUDA 1.1 release 2.0 04-01-2008 Juul VanderSpek CUDA 2.0 release nvcc.doc V2.0 ii April, 2006 Introduction Overview CUDA programming model The CUDA Toolkit targets a class of applications whose control part runs as a process on a general purpose computer (Linux, Windows), and which use one or more NVIDIA GPUs as coprocessors for accelerating SIMD parallel jobs. Such jobs are ‘self- contained’, in the sense that they can be executed and completed by a batch of GPU threads entirely without intervention by the ‘host’ process, thereby gaining optimal benefit from the parallel graphics hardware. Dispatching GPU jobs by the host process is supported by the CUDA Toolkit in the form of remote procedure calling. The GPU code is implemented as a collection of functions in a language that is essentially ‘C’, but with some annotations for distinguishing them from the host code, plus annotations for distinguishing different types of data memory that exists on the GPU. Such functions may have parameters, and they can be ‘called’ using a syntax that is very similar to regular C function calling, but slightly extended for being able to specify the matrix of GPU threads that must execute the ‘called’ function. -
High-Level Programming Model for Heterogeneous Embedded Systems Using
HIGH-LEVEL PROGRAMMING MODEL FOR HETEROGENEOUS EMBEDDED SYSTEMS USING MULTICORE INDUSTRY STANDARD APIS A Dissertation Presented to the Faculty of the Department of Computer Science University of Houston In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy By Peng Sun August 2016 HIGH-LEVEL PROGRAMMING MODEL FOR HETEROGENEOUS EMBEDDED SYSTEMS USING MULTICORE INDUSTRY STANDARD APIS Peng Sun APPROVED: Dr. Chapman, Barbara Dept. of Computer Science, University of Houston Dr. Gabriel, Edgar Dept. of Computer Science, University of Houston Dr. Shah, Shishir Dept. of Computer Science, University of Houston Dr. Subhlok, Jaspal Dept. of Computer Science, University of Houston Dr. Chandrasekaran, Sunita Dept. of CIS, University of Delaware Dean, College of Natural Sciences and Mathematics ii Acknowledgements First and foremost, I would like to thank my advisor, Dr. Barbara Chapman, for her invaluable advice and guidance in my Ph.D. study. I appreciate all her dedi- cated guidance, and great opportunities to participate in those worthwhile academic projects, and the funding to complete my Ph.D. study. Her passion and excellence on academic and contributions to communities encourage me to finish my Ph.D. degree. Specifically, I am very grateful to Dr. Sunita Chandrasekaran for the mentoring and guidance for my research and publications. That help is paramount to my Ph.D. Journey. I truly could not achieve the degree without her mentoring and advisory. Special thanks to all my committee members: Dr. Edgar Gabriel, Dr. Shishir Shah, Dr. Jaspal Subhlok, for their time, insightful comments and help. I would also like to thank my fellow labmates of the HPCTools group: Dr. -
Openss>>Expview
How to Analyze the Performance of Parallel Codes with Open|SpeedShop Jim Galarowicz: Krell Instute Don Maghrak, Krell Instute LLNL-PRES-503451 Oct 27-28, 2015 Presenters anD ExtenDeD Team v Jim Galarowicz, Krell v Donald Maghrak, Krell Open|SpeedShop extended team: v William Hachfeld, Dave Whitney, Dane Gardner: Argo Navis/Krell v Jennifer Green, David Montoya, Mike Mason, David Shrader: LANL v MarHn Schulz, MaJ Legendre and Chris Chambreau: LLNL v Mahesh Rajan, Anthony Agelastos: SNL v Dyninst group (Bart Miller: UW & Jeff Hollingsworth: UMD) v Phil Roth, Mike Brim: ORNL v Ciera Jaspan: CMU How to Analyze the Performance of Parallel CoDes with Open|SpeedShop Oct 27-28, 2015 2 Outline Welcome IntroducHon to Open|SpeedShop (O|SS) tools Overview of performance experiments Ø How to use Open|SpeedShop to gather anD Display Ø Sampling Experiments Ø Tracing Experiments Ø How to compare performance Data for Different applicaon runs Advanced and New FuncHonality v Component Based Tool Framework (CBTF) based O|SS Ø New lightweight iop anD mpip experiments Ø New: memory experiment Ø New: CUDA/GPU experiment Ø New: pthreads experiment What is new / Roadmap / Future Plans Supplemental, or Interest Ø CommanD Line Interface (CLI) tutorial anD examples How to Analyze the Performance of Parallel CoDes with Open|SpeedShop Oct 27-28, 2015 3 How to Analyze the Performance of Parallel Codes with Open|SpeedShop Secon 1 IntroDucNon into Tools anD Open|SpeeDShop Oct 27-28, 2015 Open|SpeeDShop Tool Set v Open Source Performance Analysis Tool Framework Ø Most common