Free performance data software for linux

HPL is a software package that solves a (random) dense linear system in double precision (64 bits) arithmetic on distributed-memory computers. It can thus be regarded as a portable as well as freely available implementation of the High Performance Computing Linpack Benchmark.

The algorithm used by HPL can be summarized by the following keywords: Two-dimensional block-cyclic data distribution - Right-looking variant of the LU factorization with row partial pivoting featuring multiple look-ahead depths - Recursive panel factorization with pivot search and column broadcast combined - Various virtual panel broadcast topologies - bandwidth reducing swap-broadcast algorithm - backward substitution with look-ahead of depth 1.

The HPL package provides a testing and timing program to quantify the accuracy of the obtained solution as well as the time it took to compute it. The best performance achievable by this software on your system depends on a large variety of factors.

Nonetheless, with some restrictive assumptions on the interconnection network, the algorithm described here and its attached implementation are scalable in the sense that their parallel efficiency is maintained constant with respect to the per processor memory usage.

The HPL software package requires the availibility on your system of an implementation of the Message Passing Interface MPI (1.1 compliant). An implementation of either the Basic Linear Algebra Subprograms BLAS or the Vector Signal Image Processing Library VSIPL is also needed. Machine-specific as well as generic implementations of MPI, the BLAS and VSIPL are available for a large variety of systems.

PAPI aims to provide the tool designer and application engineer with a consistent interface and methodology for use of the performance counter hardware found in most major microprocessors.
PAPI enables software engineers to see, in near real time, the relation between software performance and processor events.
The Performance API (PAPI) project specifies a standard application programming interface (API) for accessing hardware performance counters available on most modern microprocessors.
These counters exist as a small set of registers that count Events, occurrences of specific signals related to the processors function. Monitoring these events facilitates correlation between the structure of source/object code and the efficiency of the mapping of that code to the underlying architecture.
This correlation has a variety of uses in performance analysis including hand tuning, compiler optimization, debugging, benchmarking, monitoring and performance modeling. In addition, it is hoped that this information will prove useful in the development of new compilation technology as well as in steering architectural development towards alleviating commonly occurring bottlenecks in high performance computing.
PAPI provides two interfaces to the underlying counter hardware; a simple, high level interface for the acquisition of simple measurements and a fully programmable, low level interface directed towards users with more sophisticated needs.
The low level PAPI interface deals with hardware events in groups called EventSets. EventSets reflect how the counters are most frequently used, such as taking simultaneous measurements of different hardware events and relating them to one another.
For example, relating cycles to memory references or flops to level 1 cache misses can indicate poor locality and memory management. In addition, EventSets allow a highly efficient implementation which translates to more detailed and accurate measurements.
EventSets are fully programmable and have features such as guaranteed thread safety, writing of counter values, multiplexing and notification on threshold crossing, as well as processor specific features. The high level interface simply provides the ability to start, stop and read specific events, one at a time.
PAPI provides portability across different platforms. It uses the same routines with similar argument lists to control and access the counters for every architecture. As part of PAPI, we have predefined a set of events that we feel represents the lowest common denominator of every good counter implementation.
Our intent is that the same source code will count similar and possibly comparable events when run on different platforms. If the programmer chooses to use this set of standardized events, then the source code need not be changed and only a fresh compilation and link is necessary. However, should the developer wish to access machine specific events, the low level API provides access to all available events and counting modes.
If an event or feature does not exist on the current platform, PAPI returns an appropriate error code. This significantly reduces the porting effort of code using PAPI because the semantics of each call to PAPI remains the same, just the argument lists need updating. In addition to the standard set, each PAPI implementation supports all native events through the ability to directly accept platform specific counter numbers. Definitions for most, if not all of these, are included as conditional macros in the header file. In this way, PAPI avoids having inefficient code to translate all events for all platforms into a uniform representation and back again.
This translation is only done for the relatively few events defined in the standardized set. Some processors like those in the POWER series have counter groups. They enable access to specific groups of counters, instead of individual events. This presents a serious portability problem, thus PAPI abstracts hardware counters from their groups with a packed naming scheme. Each counter control value or event is made up of the counter group number and the number of the specific counter in that group.
PAPI can be divided into two layers of software. The upper layer consists of the API and machine independent support functions. The lower layer defines and exports a machine independent interface to machine dependent functions and data structures. These functions access the substrate, which may consist of the operating system, a kernel extension or assembly functions to directly access the processors registers.
PAPI tries to use the most efficient and flexible of the three, depending on what is available. Naturally, the functionality of the upper layers heavily depends on that provided by the substrate. In cases where the substrates do not provide highly desirable features, PAPI attempts to emulate them as described below.
PAPI makes sure the underlying operating system or library guards against overflow of counter values.
Each counter can potentially be incremented multiple times in a single clock cycle. This combined with increasing clock speeds and the small precision of some of the physical counters means that overflow is likely to occur.
One of the more advanced features of PAPI is to provide a portable implementation of asynchronous notification when counters exceed a user specified value.
This functionality provides the basis for PAPIs SVR4 compatible profiling calls, that generate an accurate histogram of performance interrupts based on hardware metrics, not on time. Such functionality provides the basis for all line level performance analysis software, from the antiquated days of AT&Ts prof to SGIs SpeedShop. Thus for any architecture with even the most rudimentary access to hardware performance counters, PAPI provides the foundation for a truly portable, source level, performance analysis tool based on real processor statistics.
Enhancements:
- The API was extended to decouple abstraction layers from hardware support and to provide initial support for different types of performance counters.

ProM is a new era in process mining tool support.
Process Mining research is concerned with the extraction of knowledge about a (business) process from its process execution logs. Process Mining strives to gain insight into various perspectives, such as the process (or control flow) perspective, the performance, data, and organizational perspective (The processmining.org web site has more in-depth information and scientific publications available).
ProM is an extensible framework that supports a wide variety of process mining techniques in the form of plug-ins. It is platform independent as it is implemented in Java, and can be downloaded free of charge. We welcome and support practical applications of ProM! Note that the ProM framework is issued under an open source license, namely the Common Public License (CPL), and we invite researchers and developers to contribute in the form of new plug-ins.
Currently, there are already more than 90 plug-ins available, and we support the import of (and the conversion between) several process modelling languages, such as:
- Petri nets (PNML, TPN)
- EPCs / EPKs (Aris graph format, EPML)
- YAWL
- (and many more)
There are mining plugins, such as:
- Plugins supporting control-flow mining techniques (such as the Alpha algorithm, Genetic mining, Multi-phase mining, ...)
- Plugins analysing the organizational perspective (such as the Social Network miner, the Staff Assignment miner, ...)
- Plugins dealing with the data perspective (such as the Decision miner, ...)
(and many more)
Furthermore, there are analysis plugins dealing with:
- The verification of process models (e.g., Woflan analysis)
- Verification of Linear Temporal Logic (LTL) formulas on a log
- Checking the conformance between a given process model and a log
- Performance analysis (Basic statistical analysis, and Performance Analysis with a given process model)
- Finally, ProM sports a large array of log filters, which are a valuable tool for cleaning logs from undesired, or unimportant, artefacts.
Enhancements:
- This release features fundamental framework improvements and the addition of a major set of new plugins.
- The performance of log reading has been improved dramatically, introducing live modification of logs and random access.
- More than 70 newly added plugins substantially extend the analysis feature set.

MySpace Data Mining Tools are a set of Java classes designed to mine information from MySpace profile and blog pages using a multi-threaded Web page access method.
Enhancements:
- Direct database connectivity via JDBC was implemented for data storage.
- A basic user profile class was created to handle both user data compression and database access.
- Minor bugs were fixed for some of the raw data accessing routines.

File::Data is a Perl module as a interface to file data.

Wraps all the accessing of a file into a convenient set of calls for reading and writing data, including a simple regex interface.

Note that the file needs to exist prior to using this module!

See new()

SYNOPSIS

use strict;

use File::Data;

my $o_dat = File::Data->new(./t/example);

$o_dat->write("complete file contentsn");

$o_dat->prepend("first linen"); # line 0

$o_dat->append("original second (last) linen");

$o_dat->insert(2, "new second linen"); # inc. zero!

$o_dat->replace(line, LINE);

print $o_dat->READ;

Or, perhaps more seriously :-}

my $o_sgm = File::Data->new(./sgmlfile);

print "new SGML data: ".$o_sgm->REPLACE(
s*((?s).*)s* ,
qq| key="val" |,
) if $o_sgm;

See METHODS and EXAMPLES.

IMPORTANT

lowercase method calls return the object itself, so you can chain calls.

my $o_obj = $o_dat->read; # ! READ; # !<<less