Talend Out of Memory Read From File

Question & Answer

Question

Tin you provide some guidelines for diagnosing CPLEX error 1001: out of memory?

Answer

CPLEX Error 1001 (out of memory) tin can occur for many reasons. Some detail causes are discussed in documents titled:

• Guidelines for estimating CPLEX memory requirements based on problem size
• 32-bit AIX application retention usage
• How to extend 32-bit CPLEX on Microsoft Windows use more than 2GB or RAM

Rather than discuss individual causes, this document considers more full general tactics to help identify the source of the memory limitation. In some cases, you might find that the application really does need more memory to run to completion. But, in others, you might detect that a uncomplicated modify to auto configuration, the mathematical model, the code that expresses the model, or CPLEX parameter settings volition drastically reduce retentivity use, enabling your application to run much more efficiently.

Out of retentivity errors in a CPLEX application typically fall into one of four categories, each of which is discussed in more item below.

• Unexpected retention limits imposed past the operating arrangement
• Unexpectedly large memory employ by the application that calls CPLEX
• Unnecessary memory use during the modeling phase
• Excessive retention utilise by CPLEX optimization routines

Unexpected memory limits imposed past the operating system

An application may run out of retentiveness unexpectedly because information technology cannot access all available memory on the computer. Possible reasons include:

• Limits on private accounts or processes. Use the 'ulimit -a' command on UNIX or the Windows Task Director to check resources available for the private account.
• Retention restrictions imposed by the compiler, linker, or operating system. For example, programs compiled and linked as standard 32-scrap applications using default options tin can access no more 2 gigabytes of memory, regardless of the amount of retentiveness available. And, 32-bit applications on AIX accept a default of 256 MB of allocatable memory. (Read 32-bit AIX application retentivity usage to come across how to increase this limit). When a 32-bit awarding imposes a retentivity limit on memory that CPLEX uses, consider moving to a 64-scrap operating organisation instead.
• Various other factors, such as multiple users on the computer or overly restrictive virtual retention configurations.

Tools (like 'top' and 'vmstat' on UNIX or Windows Task Managing director) can assistance you isolate these potential bug. Too, using a program that merely allocates, initializes, and frees arbitrary amounts of memory can help decide whether your problem is specific to your CPLEX awarding or more general. Click here - testmalloc.c to download such a programme.

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Unexpectedly large memory apply by the application that calls CPLEX

When a CPLEX based application runs out of retention, you lot need to find out whether the majority of the memory consumption occurs inside CPLEX or in the invoking application. If CPLEX inherits very little free retention from the calling application, CPLEX volition probably issue an out-of-retention error message. Yet potential retentiveness reductions are more than likely to occur in the code surrounding CPLEX. The post-obit tactics tin aid you assess the distribution of memory use amidst the various parts of the awarding.

• Monitor the retentiveness consumption of the program every bit it proceeds, detecting spikes in utilize to isolate the parts of the awarding that may swallow excessive memory.
• Compare retention apply of your CPLEX application with interactive CPLEX or a simple example program on the same model. This comparing tin assist you determine how much memory CPLEX volition demand to solve the model. Contrast that amount with the full retentiveness apply of your awarding to determine whether memory apply outside of CPLEX is reasonable.
• Employ tools like Purify, Insure, Premises Checker, and Valgrind to bank check for memory leaks in the application.
• Make sure y'all solve the intended model. If the modeling code inadvertently creates a much larger model, the retentivity use may too increase. By writing out a SAV file of the model and reading it into interactive CPLEX, you lot tin can and so examine the model statistics (by turning the datacheck parameter on and using the display problem stats command) to verify they are consistent with the intended model.

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Unnecessary retentivity utilize during the modeling phase

When writing lawmaking to build the model you lot will give to CPLEX, make certain you don't create or retain problem data that is no longer needed after CPLEX has received or extracted the model. In the C API, CPLEX makes copies of any arrays passed to problem-creation or modification routines, then yous can gratuitous the corresponding arrays in your programs if you don't need them anymore. In C++, inadvertent calls to re-create constructors or lazy copy mechanisms can result in unnecessary retention use. In Java, using the class IloLPMatrix (or the equivalent method of ILPMatrix in .Internet) has the potential to salve retentiveness during model generation.

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Excessive memory use by CPLEX optimization routines

If you have determined that the bulk of the retention use occurs in one of the CPLEX optimization algorithms, yous need to decide whether memory use by CPLEX is reasonable given the trouble size. For linear programs, a reasonable judge of memory utilise is to permit one megabyte per yard constraints (or, equivalently, one gigabyte per one thousand thousand constraints). For integer programs, no unproblematic formula exists since memory utilize depends and so heavily on the size of the branch and spring tree. But, the guess for linear programs still provides a lower bound. For quadratic programs, a slightly stronger lower bound derives from calculation the number of constraints and variables, then allow one megabyte for every 1000.

If these estimates reveal that the requirements of CPLEX dramatically exceed the available retentivity on the machine, then consider calculation more memory to the machine, or moving from a 32-bit to 64-bit operating organization. If the CPLEX requirements just barely exceed available retentivity, you may be able to reduce CPLEX retentiveness apply by changing some of its parameter settings, including the following. Refer to the ILOG CPLEX Reference Transmission if you need boosted information well-nigh these parameters.

• Users of CPLEX 10.0 and later should turn CPLEX's memory emphasis parameter on. This will instruct CPLEX to ready various internal parameters to conserve memory. This setting may upshot in a slight increment in run fourth dimension, but typically it is negligible. However, when solving linear programs with one of CPLEX's simplex methods, this setting limits solution information that requires a factorized basis (for example, sensitivity assay or the basis condition number). Users of CPLEX 9.ane and earlier practise non have admission to the memory accent parameter, but they can attain many of the same benefits by turning on the presolve shrink, bulwark out of core, or simplex finalfactor parameters (all of which were consolidated into the memory emphasis parameter for version ten.0). Every bit a last resort, you tin really turn presolve off. This will salve more memory, but it can upshot in dramatic increases in solve-times for many models.
• For linear and quadratic programs, consider using a different algorithm, equally each algorithm uses memory differently. Similarly, setting the presolve dual parameter to on for linear programs tin salvage memory. The memory required to solve the dual model may differ significantly from that required to solve the cardinal.
• For mixed integer programs, the size of the branch and cut tree dominates memory use across that required to solve the initial LP relaxation. Anything yous can do to reduce the size of the branch and cut tree volition assist. And so, improving the run time will often too reduce memory. Read the document titled CPLEX Operation Tuning for Mixed Integer Programs for a give-and-take of MIP performance tuning. In addition, consider setting the node file parameter to 3 to write the nodes to deejay. This will save memory with minimal overhead.
• If you lot are taking advantage of the parallel CPLEX optimizers, reducing the number of threads in use typically likewise reduces memory usage. Even so, this may adversely impact operation, in which example one should view this as a short term solution.
• If yous are using the solution pool feature and CPLEX generates a huge number of solutions, storing those solutions may dominate retentiveness usage. In that case, set the solution pool chapters number to a minor value.
• If all else fails, you lot might exist able to create an equivalent formulation of the model that requires less memory.

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cplex/Document/134

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Source: https://www.ibm.com/support/pages/diagnosing-cplex-error-1001-out-memory

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