RELEASE: A High-level Paradigm for Reliable Large-scale Server Software

Erlang is a functional language with a much-emulated model for building reliable distributed systems. This paper outlines the RELEASE project, and describes the progress in the rst six months. The project aim is to scale the Erlang's radical concurrency-oriented programming paradigm to build reliable general-purpose software, such as server-based systems, on massively parallel machines. Currently Erlang has inherently scalable computation and reliability models, but in practice scalability is constrained by aspects of the language and virtual machine. We are working at three levels to address these challenges: evolving the Erlang virtual machine so that it can work effectively on large scale multicore systems; evolving the language to Scalable Distributed (SD) Erlang; developing a scalable Erlang infrastructure to integrate multiple, heterogeneous clusters. We are also developing state of the art tools that allow programmers to understand the behaviour of massively parallel SD Erlang programs. We will demonstrate the e ectiveness of the RELEASE approach using demonstrators and two large case studies on a Blue Gene

RELEASE: A High-level Paradigm for Reliable Large-scale Server Software

Erlang is a functional language with a much-emulated model for building reliable distributed systems. This paper outlines the RELEASE project, and describes the progress in the first six months. The project aim is to scale the Erlang’s radical concurrency-oriented programming paradigm to build reliable general-purpose software, such as server-based systems, on massively parallel machines. Currently Erlang has inherently scalable computation and reliability models, but in practice scalability is constrained by aspects of the language and virtual machine. We are working at three levels to address these challenges: evolving the Erlang virtual machine so that it can work effectively on large scale multicore systems; evolving the language to Scalable Distributed (SD) Erlang; developing a scalable Erlang infrastructure to integrate multiple, heterogeneous clusters. We are also developing state of the art tools that allow programmers to understand the behaviour of massively parallel SD Erlang programs. We will demonstrate the effectiveness of the RELEASE approach using demonstrators and two large case studies on a Blue Gene

Scalable Applications on Heterogeneous System Architectures: A Systematic Performance Analysis Framework

The efficient parallel execution of scientific applications is a key challenge in high-performance computing (HPC). With growing parallelism and heterogeneity of compute resources as well as increasingly complex software, performance analysis has become an indispensable tool in the development and optimization of parallel programs. This thesis presents a framework for systematic performance analysis of scalable, heterogeneous applications. Based on event traces, it automatically detects the critical path and inefficiencies that result in waiting or idle time, e.g. due to load imbalances between parallel execution streams. As a prerequisite for the analysis of heterogeneous programs, this thesis specifies inefficiency patterns for computation offloading. Furthermore, an essential contribution was made to the development of tool interfaces for OpenACC and OpenMP, which enable a portable data acquisition and a subsequent analysis for programs with offload directives. At present, these interfaces are already part of the latest OpenACC and OpenMP API specification. The aforementioned work, existing preliminary work, and established analysis methods are combined into a generic analysis process, which can be applied across programming models. Based on the detection of wait or idle states, which can propagate over several levels of parallelism, the analysis identifies wasted computing resources and their root cause as well as the critical-path share for each program region. Thus, it determines the influence of program regions on the load balancing between execution streams and the program runtime. The analysis results include a summary of the detected inefficiency patterns and a program trace, enhanced with information about wait states, their cause, and the critical path. In addition, a ranking, based on the amount of waiting time a program region caused on the critical path, highlights program regions that are relevant for program optimization. The scalability of the proposed performance analysis and its implementation is demonstrated using High-Performance Linpack (HPL), while the analysis results are validated with synthetic programs. A scientific application that uses MPI, OpenMP, and CUDA simultaneously is investigated in order to show the applicability of the analysis