Investigating tools and techniques for improving software performance on multiprocessor computer systems

The availability of modern commodity multicore processors and multiprocessor computer systems has resulted in the widespread adoption of parallel computers in a variety of environments, ranging from the home to workstation and server environments in particular. Unfortunately, parallel programming is harder and requires more expertise than the traditional sequential programming model. The variety of tools and parallel programming models available to the programmer further complicates the issue. The primary goal of this research was to identify and describe a selection of parallel programming tools and techniques to aid novice parallel programmers in the process of developing efficient parallel C/C++ programs for the Linux platform. This was achieved by highlighting and describing the key concepts and hardware factors that affect parallel programming, providing a brief survey of commonly available software development tools and parallel programming models and libraries, and presenting structured approaches to software performance tuning and parallel programming. Finally, the performance of several parallel programming models and libraries was investigated, along with the programming effort required to implement solutions using the respective models. A quantitative research methodology was applied to the investigation of the performance and programming effort associated with the selected parallel programming models and libraries, which included automatic parallelisation by the compiler, Boost Threads, Cilk Plus, OpenMP, POSIX threads (Pthreads), and Threading Building Blocks (TBB). Additionally, the performance of the GNU C/C++ and Intel C/C++ compilers was examined. The results revealed that the choice of parallel programming model or library is dependent on the type of problem being solved and that there is no overall best choice for all classes of problem. However, the results also indicate that parallel programming models with higher levels of abstraction require less programming effort and provide similar performance compared to explicit threading models. The principle conclusion was that the problem analysis and parallel design are an important factor in the selection of the parallel programming model and tools, but that models with higher levels of abstractions, such as OpenMP and Threading Building Blocks, are favoured

Quantifying and Predicting the Influence of Execution Platform on Software Component Performance

The performance of software components depends on several factors, including the execution platform on which the software components run. To simplify cross-platform performance prediction in relocation and sizing scenarios, a novel approach is introduced in this thesis which separates the application performance profile from the platform performance profile. The approach is evaluated using transparent instrumentation of Java applications and with automated benchmarks for Java Virtual Machines

Quantifying and Predicting the Influence of Execution Platform on Software Component Performance

The performance of software components depends on several factors, including the execution platform on which the software components run. To simplify cross-platform performance prediction in relocation and sizing scenarios, a novel approach is introduced in this thesis which separates the application performance profile from the platform performance profile. The approach is evaluated using transparent instrumentation of Java applications and with automated benchmarks for Java Virtual Machines

Towards instantaneous performance analysis using coarse-grain sampled and instrumented data

Nowadays, supercomputers deliver an enormous amount of computation power; however, it is well-known that applications only reach a fraction of it. One limiting factor is the single processor performance because it ultimately dictates the overall achieved performance. Performance analysis tools help locating performance inefficiencies and their nature to ultimately improve the application performance. Performance tools rely on two collection techniques to invoke their performance monitors: instrumentation and sampling. Instrumentation refers to inject performance monitors into concrete application locations whereas sampling invokes the installed monitors to external events. Each technique has its advantages. The measurements obtained through instrumentation are directly associated to the application structure while sampling allows a simple way to determine the volume of measurements captured. However, the granularity of the measurements that provides valuable insight cannot be determined a priori. Should analysts study the performance of an application for the first time, they may consider using a performance tool and instrument every routine or use high-frequency sampling rates to provide the most detailed results. These approaches frequently lead to large overheads that impact the application performance and thus alter the measurements gathered and, therefore, mislead the analyst. This thesis introduces the folding mechanism that takes advantage of the repetitiveness found in many applications. The mechanism smartly combines metrics captured through coarse-grain sampling and instrumentation mechanisms to provide instantaneous metric reports within instrumented regions and without perturbing the application execution. To produce these reports, the folding processes metrics from different type of sources: performance and energy counters, source code and memory references. The process depends on their nature. While performance and energy counters represent continuous metrics, the source code and memory references refer to discrete values that point out locations within the application code or address space. This thesis evaluates and validates two fitting algorithms used in different areas to report continuous metrics: a Gaussian interpolation process known as Kriging and piece-wise linear regressions. The folding also takes benefit of analytical performance models to focus on a small set of performance metrics instead of exploring a myriad of performance counters. The folding also correlates the metrics with the source-code using two alternatives: using the outcome of the piece-wise linear regressions and a mechanism inspired by Multi-Sequence Alignment techniques. Finally, this thesis explores the applicability of the folding mechanism to captured memory references to detail which and how data objects are accessed. This thesis proposes an analysis methodology for parallel applications that focus on describing the most time-consuming computing regions. It is implemented on top of a framework that relies on a previously existing clustering tool and the folding mechanism. To show the usefulness of the methodology and the framework, this thesis includes the discussion of multiple first-time seen in-production applications. The discussions include high level of detail regarding the application performance bottlenecks and their responsible code. Despite many analyzed applications have been compiled using aggressive compiler optimization flags, the insight obtained from the folding mechanism has turned into small code transformations based on widely-known optimization techniques that have improved the performance in some cases. Additionally, this work also depicts power monitoring capabilities of recent processors and discusses the simultaneous performance and energy behavior on a selection of benchmarks and in-production applications.Actualment, els supercomputadors ofereixen una àmplia potència de càlcul però les aplicacions només en fan servir una petita fracció. Un dels factors limitants és el rendiment d'un processador, el qual dicta el rendiment en general. Les eines d'anàlisi de rendiment ajuden a localitzar els colls d'ampolla i la seva natura per a, eventualment, millorar el rendiment de l'aplicació. Les eines d'anàlisi de rendiment empren dues tècniques de recol·lecció de dades: instrumentació i mostreig. La instrumentació es refereix a la capacitat d'injectar monitors en llocs específics del codi mentre que el mostreig invoca els monitors quan ocórren esdeveniments externs. Cadascuna d'aquestes tècniques té les seves avantatges. Les mesures obtingudes per instrumentació s'associen directament a l'estructura de l'aplicació mentre que les obtingudes per mostreig permeten una forma senzilla de determinar-ne el volum capturat. Sigui com sigui, la granularitat de les mesures no es pot determinar a priori. Conseqüentment, si un analista vol estudiar el rendiment d'una aplicació sense saber-ne res, hauria de considerar emprar una eina d'anàlisi i instrumentar cadascuna de les rutines o bé emprar freqüències de mostreig altes per a proveir resultats detallats. En qualsevol cas, aquestes alternatives impacten en el rendiment de l'aplicació i per tant alterar les mètriques capturades, i conseqüentment, confondre a l'analista. Aquesta tesi introdueix el mecanisme anomenat folding, el qual aprofita la repetitibilitat existent en moltes aplicacions. El mecanisme combina intel·ligentment mètriques obtingudes mitjançant mostreig de gra gruixut i instrumentació per a proveir informes de mètriques instantànies dins de regions instrumentades sense pertorbar-ne l'execució. Per a produir aquests informes, el mecanisme processa les mètriques de diferents fonts: comptadors de rendiment i energia, codi font i referències de memoria. El procés depen de la natura de les dades. Mentre que les mètriques de rendiment i energia són valors continus, el codi font i les referències de memòria representen valors discrets que apunten ubicacions dins el codi font o l'espai d'adreces. Aquesta tesi evalua i valida dos algorismes d'ajust: un procés d'interpolació anomenat Kriging i una interpolació basada en regressions lineals segmentades. El mecanisme de folding també s'aprofita de models analítics de rendiment basats en comptadors hardware per a proveir un conjunt reduït de mètriques enlloc d'haver d'explorar una multitud de comptadors. El mecanisme també correlaciona les mètriques amb el codi font emprant dues alternatives: per un costat s'aprofita dels resultats obtinguts per les regressions lineals segmentades i per l'altre defineix un mecanisme basat en tècniques d'alineament de multiples seqüències. Aquesta tesi també explora l'aplicabilitat del mecanisme per a referències de memoria per a informar quines i com s'accessedeixen les dades de l'aplicació. Aquesta tesi proposa una metodología d'anàlisi per a aplicacions paral·leles centrant-se en descriure les regions de càlcul que consumeixen més temps. La metodología s'implementa en un entorn de treball que usa un mecanisme de clustering preexistent i el mecanisme de folding. Per a demostrar-ne la seva utilitat, aquesta tesi inclou la discussió de múltiples aplicacions analitzades per primera vegada. Les discussions inclouen un alt nivel de detall en referencia als colls d'ampolla de les aplicacions i de la seva natura. Tot i que moltes d'aquestes aplicacions s'han compilat amb opcions d'optimització agressives, la informació obtinguda per l'entorn de treball es tradueix en petites modificacions basades en tècniques d'optimització que permeten millorar-ne el rendiment en alguns casos. Addicionalment, aquesta tesi també reporta informació sobre el consum energètic reportat per processadors recents i discuteix el comportament simultani d'energia i rendiment en una selecció d'aplicacions sintètiques i aplicacions en producció