Mailman 3 May 2013 - SciPy-Dev

FAIL: test_qz_complex64 (test_decomp.TestQZ)
by Nils Wagner May 5, 2013

May 5, 2013

Hi all, I found a (new) test failure. Seems to be an accuracy issue. >>> scipy.__version__ '0.13.0.dev-ccbdff8' >>> numpy.__version__ '1.8.0.dev-004ce27' >>> scipy.show_config() umfpack_info: NOT AVAILABLE atlas_threads_info: libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas'] library_dirs = ['/home/nils/local/lib/'] define_macros = [('ATLAS_INFO', '"\\"3.10.1\\""')] language = f77 blas_opt_info: libraries = ['ptf77blas', 'ptcblas', 'atlas'] library_dirs = ['/home/nils/local/lib/'] define_macros = [('ATLAS_INFO', '"\\"3.10.1\\""')] language = c atlas_blas_threads_info: libraries = ['ptf77blas', 'ptcblas', 'atlas'] library_dirs = ['/home/nils/local/lib/'] define_macros = [('ATLAS_INFO', '"\\"3.10.1\\""')] language = c lapack_opt_info: libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas'] library_dirs = ['/home/nils/local/lib/'] define_macros = [('ATLAS_INFO', '"\\"3.10.1\\""')] language = f77 lapack_mkl_info: NOT AVAILABLE blas_mkl_info: NOT AVAILABLE mkl_info: NOT AVAILABLE ====================================================================== FAIL: test_qz_complex64 (test_decomp.TestQZ) ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/nils/local/lib64/python2.7/site-packages/scipy/linalg/tests/test_decomp.py", line 1765, in test_qz_complex64 assert_array_almost_equal(dot(dot(Q,AA),Z.conjugate().T), A) File "/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py", line 818, in assert_array_almost_equal header=('Arrays are not almost equal to %d decimals' % decimal)) File "/home/nils/local/lib64/python2.7/site-packages/numpy/testing/utils.py", line 651, in assert_array_compare raise AssertionError(msg) AssertionError: Arrays are not almost equal to 6 decimals (mismatch 4.0%) x: array([[ 0.92961562+0.72968888j, 0.31637603+0.99401349j, 0.18391919+0.67687315j, 0.20456034+0.79082197j, 0.56772506+0.17091417j],... y: array([[ 0.92961609+0.72968906j, 0.31637555+0.99401456j, 0.18391882+0.67687368j, 0.20456028+0.79082251j, 0.56772500+0.17091426j],... ---------------------------------------------------------------------- Ran 6411 tests in 208.475s FAILED (KNOWNFAIL=28, SKIP=158, failures=1) <nose.result.TextTestResult run=6411 errors=0 failures=1>

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Licensing issue with AMOS library
by Raziman T V May 4, 2013

May 4, 2013

Hi, The amos library for computing special functions is part of scipy.special. If I understand correctly, routines in the library are being used to compute Bessel function values for complex arguments. However, a version of the algorithm is seen to have been published on TOMS ( http://dl.acm.org/citation.cfm?id=214331). According to the ACM software license, the software isn't really "free" since commercial use is not allowed. How does this work with scipy? Any clarification on the license status of the library will be greatly appreciated since I plan to use it in a routine I intend to publish. Thank you Yours Raziman

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CfP 2013 Workshop on Middleware for HPC and Big Data Systems (MHPC'13)
by MHPC 2013 May 3, 2013

May 3, 2013

we apologize if you receive multiple copies of this message =================================================================== CALL FOR PAPERS 2013 Workshop on Middleware for HPC and Big Data Systems MHPC '13 as part of Euro-Par 2013, Aachen, Germany =================================================================== Date: August 27, 2012 Workshop URL: http://m-hpc.org Springer LNCS SUBMISSION DEADLINE: May 31, 2013 - LNCS Full paper submission (rolling abstract submission) June 28, 2013 - Lightning Talk abstracts SCOPE Extremely large, diverse, and complex data sets are generated from scientific applications, the Internet, social media and other applications. Data may be physically distributed and shared by an ever larger community. Collecting, aggregating, storing and analyzing large data volumes presents major challenges. Processing such amounts of data efficiently has been an issue to scientific discovery and technological advancement. In addition, making the data accessible, understandable and interoperable includes unsolved problems. Novel middleware architectures, algorithms, and application development frameworks are required. In this workshop we are particularly interested in original work at the intersection of HPC and Big Data with regard to middleware handling and optimizations. Scope is existing and proposed middleware for HPC and big data, including analytics libraries and frameworks. The goal of this workshop is to bring together software architects, middleware and framework developers, data-intensive application developers as well as users from the scientific and engineering community to exchange their experience in processing large datasets and to report their scientific achievement and innovative ideas. The workshop also offers a dedicated forum for these researchers to access the state of the art, to discuss problems and requirements, to identify gaps in current and planned designs, and to collaborate in strategies for scalable data-intensive computing. The workshop will be one day in length, composed of 20 min paper presentations, each followed by 10 min discussion sections. Presentations may be accompanied by interactive demonstrations. TOPICS Topics of interest include, but are not limited to: - Middleware including: Hadoop, Apache Drill, YARN, Spark/Shark, Hive, Pig, Sqoop, HBase, HDFS, S4, CIEL, Oozie, Impala, Storm and Hyrack - Data intensive middleware architecture - Libraries/Frameworks including: Apache Mahout, Giraph, UIMA and GraphLab - NG Databases including Apache Cassandra, MongoDB and CouchDB/Couchbase - Schedulers including Cascading - Middleware for optimized data locality/in-place data processing - Data handling middleware for deployment in virtualized HPC environments - Parallelization and distributed processing architectures at the middleware level - Integration with cloud middleware and application servers - Runtime environments and system level support for data-intensive computing - Skeletons and patterns - Checkpointing - Programming models and languages - Big Data ETL - Stream processing middleware - In-memory databases for HPC - Scalability and interoperability - Large-scale data storage and distributed file systems - Content-centric addressing and networking - Execution engines, languages and environments including CIEL/Skywriting - Performance analysis, evaluation of data-intensive middleware - In-depth analysis and performance optimizations in existing data-handling middleware, focusing on indexing/fast storing or retrieval between compute and storage nodes - Highly scalable middleware optimized for minimum communication - Use cases and experience for popular Big Data middleware - Middleware security, privacy and trust architectures DATES Papers: Rolling abstract submission May 31, 2013 - Full paper submission July 8, 2013 - Acceptance notification October 3, 2013 - Camera-ready version due Lightning Talks: June 28, 2013 - Deadline for lightning talk abstracts July 15, 2013 - Lightning talk notification August 27, 2013 - Workshop Date TPC CHAIR Michael Alexander (chair), TU Wien, Austria Anastassios Nanos (co-chair), NTUA, Greece Jie Tao (co-chair), Karlsruhe Institut of Technology, Germany Lizhe Wang (co-chair), Chinese Academy of Sciences, China Gianluigi Zanetti (co-chair), CRS4, Italy PROGRAM COMMITTEE Amitanand Aiyer, Facebook, USA Costas Bekas, IBM, Switzerland Jakob Blomer, CERN, Switzerland William Gardner, University of Guelph, Canada José Gracia, HPC Center of the University of Stuttgart, Germany Zhenghua Guom, Indiana University, USA Marcus Hardt, Karlsruhe Institute of Technology, Germany Sverre Jarp, CERN, Switzerland Christopher Jung, Karlsruhe Institute of Technology, Germany Andreas Knüpfer - Technische Universität Dresden, Germany Nectarios Koziris, National Technical University of Athens, Greece Yan Ma, Chinese Academy of Sciences, China Martin Schulz - Lawrence Livermore National Laboratory Viral Shah, MIT Julia Group, USA Dimitrios Tsoumakos, Ionian University, Greece Zhifeng Yun, Louisiana State University, USA PAPER PUBLICATION Accepted full papers will be published in the Springer LNCS series. The best papers of the workshop -- after extension and revision -- will be published in a Special Issue of the Springer Journal of Scalable Computing. PAPER SUBMISSION Papers submitted to the workshop will be reviewed by at least two members of the program committee and external reviewers. Submissions should include abstract, key words, the e-mail address of the corresponding author, and must not exceed 10 pages, including tables and figures at a main font size no smaller than 11 point. Submission of a paper should be regarded as a commitment that, should the paper be accepted, at least one of the authors will register and attend the conference to present the work. The format must be according to the Springer LNCS Style. Initial submissions are in PDF; authors of accepted papers will be requested to provide source files. Format Guidelines: http://www.springer.de/comp/lncs/authors.html Style template: ftp://ftp.springer.de/pub/tex/latex/llncs/latex2e/llncs2e.zip Abstract Registration - Submission Link: http://edas.info/newPaper.php?c=14763 LIGHTNING TALKS Talks are strictly limited to 5 minutes. They can be used to gain early feedback on ongoing research, for demonstrations, to present research results, early research ideas, perspectives and positions of interest to the community. Lightning talks should spark discussion with presenters making themselves available following the lightning talk track. DURATION: Workshop Duration is one day. GENERAL INFORMATION The workshop will be held as part of Euro-Par 2013. Euro-Par 2013: http://www.europar2013.org

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playing with intel ipp (signal processing) library
by Neal Becker May 2, 2013

May 2, 2013

I spent a little time wrapping some of intel ipp signal processing library and benchmarking against my hand-written c++ code. So far I've tried some of the FIR filtering (single and multi-rate), and correlation. I was surprised to find that the ipp components were 3-10 times the speed of my c++ code. Perhaps others might want to look at using these components. Of course, they are not free - but this is in the same class as building numpy/scipy using MKL.

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