A trace-scaling agent for parallel application tracing.

Tracing and performance analysis tools are an important component in the development of high performance applications. Tracing parallel programs with current tracing tools, however, easily leads to large trace files with hundreds of Megabytes. The storage, visualization, and analysis of such trace files is often difficult. We propose a trace-scaling agent for tracing parallel applications, which learns the application behavior in runtime and achieves a small, easy to handle trace. The agent dynamically identifies the amount of information needed to capture the application behavior. This knowledge acquired at runtime allows recording only the non-iterative trace information, which drastically reduces the size of the trace file.Peer Reviewe

Exploring the predictability of MPI messages

Scalability to a large number of processes is one of the weaknesses of current MPI implementations. Standard implementations are able to scale to hundreds of nodes, but none beyond that. The main problem of current implementations is that performance is more important than scalability and thus some assumptions about resources are taken that will not scale well. The objective of the paper is twofold. On one hand, we show that characteristics such as the size and the sender of MPI messages are very predictable (accuracy above 90%). On the other hand, we present some examples where current MPI implementations would not work well when run on a large configuration and how this predictability could be used to solve the scalability problem.This work has been supported by the Ministry of Science and Technology of Spain and the European Union (FEDER funds) under contract TIC2001-0995-C02-01.Peer ReviewedPostprint (author's final draft

Data mining temporal and indefinite relations with numerical dependencies

Available from British Library Document Supply Centre-DSC:DXN039798 / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

On the scalability of tracing mechanisms.

Performance analysis tools are an important component of the parallel program development and tuning cycle. To obtain the raw performance data, an instrumented application is run with probes that take measures of specific events or performance indicators. Tracing parallel programs can easily lead to huge trace files of hundreds of Megabytes. Several problems arise in this context: The storage requirement of the high number of traces from executions under slightly changed conditions; visualization packages have difficulties in showing large traces efficiently leading to slow response time; large trace files often contain huge amounts of redundant information. In this paper we propose and evaluate a dynamic scalable tracing mechanism for OpenMP based parallel applications. Our results show: With scaled tracing the size of the trace files becomes significantly reduced. The scaled traces contain only the non-iterative data. The scaled trace reveals important performance information faster to the performance analyst and identifies the application structure.Peer Reviewe

A trace-scaling agent for parallel application tracing.

Tracing and performance analysis tools are an important component in the development of high performance applications. Tracing parallel programs with current tracing tools, however, easily leads to large trace files with hundreds of Megabytes. The storage, visualization, and analysis of such trace files is often difficult. We propose a trace-scaling agent for tracing parallel applications, which learns the application behavior in runtime and achieves a small, easy to handle trace. The agent dynamically identifies the amount of information needed to capture the application behavior. This knowledge acquired at runtime allows recording only the non-iterative trace information, which drastically reduces the size of the trace file.Peer Reviewe

A trace-scaling agent for parallel application tracing.

Tracing and performance analysis tools are an important component in the development of high performance applications. Tracing parallel programs with current tracing tools, however, easily leads to large trace files with hundreds of Megabytes. The storage, visualization, and analysis of such trace files is often difficult. We propose a trace-scaling agent for tracing parallel applications, which learns the application behavior in runtime and achieves a small, easy to handle trace. The agent dynamically identifies the amount of information needed to capture the application behavior. This knowledge acquired at runtime allows recording only the non-iterative trace information, which drastically reduces the size of the trace file.Peer Reviewe

Exploring the predictability of MPI messages.

Scalability to a large number of processes is one of the weaknesses of current MPI implementations. Standard implementations are able to scale to hundreds of nodes, but no beyond that. The main problem of current implementations is that performance is more important than scalability and thus some assumptions about resources are taken that will not scale well. The objective of this paper is twofold. On one hand, we show that characteristics such as the size and the sender of MPI messages are very predictable (accuracy above 90%). On the other hand, we present some examples where current MPI implementations would not work well when run on a large configuration and how this predictability could be used to solve the scalability problem.Peer Reviewe