Achieving practical exascale supercomputing will require massive increases in energy efficiency. The bulk of this improvement will likely be derived from hardware advances such as improved semiconductor device technologies and tighter integration, hopefully resulting in more energy efficient computer architectures. Still, software will have an important role to play. With every generation of new hardware, more power measurement and control capabilities are exposed. Many of these features require software involvement to maximize feature benefits. This trend will allow algorithm designers to add power and energy efficiency to their optimization criteria. Similarly, at the system level, opportunities now exist for energy-aware scheduling to meet external utility constraints such as time of day cost charging and power ramp rate limitations. Finally, future architectures might not be able to operate all components at full capability for a range of reasons including temperature considerations or power delivery limitations. Software will need to make appropriate choices about how to allocate the available power budget given many, sometimes conflicting considerations.
The Bulk Synchronous Parallel programming model is showing performance limitations at high processor counts. We propose over-decomposition of the domain, operated on as tasks, to smooth out utilization of the computing resource, in particular the node interconnect and processing cores, and hide intra- and inter-node data movement. Our approach maintains the existing coding style commonly employed in computational science and engineering applications. Although we show improved performance on existing computers, up to 131,072 processor cores, the effectiveness of this approach on expected future architectures will require the continued evolution of capabilities throughout the codesign stack. Success then will not only result in decreased time to solution, but would also make better use of the hardware capabilities and reduce power and energy requirements, while fundamentally maintaining the current code configuration strategy.
The metrics used for evaluating energy saving techniques for future HPC systems are critical to the correct assessment of proposed methods. Current predictions forecast that overcoming reduced system reliability, increased power requirements and energy consumption will be a major design challenge for future systems. Modern runtime energy-saving research efforts do not take into account the energy spent providing reliability. They also do not account for the increase in the probability of failure during application execution due to runtime overhead from energy saving methods. While this is very reasonable for current systems, it is insufficient for future generation systems. By taking into account the energy consumption ramifications of increased runtimes on system reliability, better energy saving techniques can be developed. This paper demonstrates how to determine the impact of runtime energy conservation methods within the context of failure-prone large scale systems. In addition, a survey of several energy savings methodologies is conducted and an analysis is performed with respect to their effectiveness in an environment in which failures occur.
Power and energy concerns are motivating chip manufacturers to consider future hybrid-core processor designs that may combine a small number of traditional cores optimized for single-thread performance with a large number of simpler cores optimized for throughput performance. This trend is likely to impact the way in which compute resources for network protocol processing functions are allocated and managed. In particular, the performance of MPI match processing is critical to achieving high message throughput. In this paper, we analyze the ability of simple and more complex cores to perform MPI matching operations for various scenarios in order to gain insight into how MPI implementations for future hybrid-core processors should be designed.
MPI defines a one-to-one relationship between MPI processes and ranks. This model captures many use cases effectively; however, it also limits communication concurrency and interoperability between MPI and programming models that utilize threads. Our paper describes the MPI endpoints extension, which relaxes the longstanding one-to-one relationship between MPI processes and ranks. Using endpoints, an MPI implementation can map separate communication contexts to threads, allowing them to drive communication independently. Also, endpoints enable threads to be addressable in MPI operations, enhancing interoperability between MPI and other programming models. Furthermore, these characteristics are illustrated through several examples and an empirical study that contrasts current multithreaded communication performance with the need for high degrees of communication concurrency to achieve peak communication performance.
Measuring and controlling the power and energy consumption of high performance computing systems by various components in the software stack is an active research area [13, 3, 5, 10, 4, 21, 19, 16, 7, 17, 20, 18, 11, 1, 6, 14, 12]. Implementations in lower level software layers are beginning to emerge in some production systems, which is very welcome. To be most effective, a portable interface to measurement and control features would significantly facilitate participation by all levels of the software stack. We present a proposal for a standard power Application Programming Interface (API) that endeavors to cover the entire software space, from generic hardware interfaces to the input from the computer facility manager.
Proceedings of ExaMPI 2014: Exascale MPI 2014 - held in conjunction with SC 2014: The International Conference for High Performance Computing, Networking, Storage and Analysis
Advances in node-level architecture and interconnect technology needed to reach extreme scale necessitate a reevaluation of long-standing models of computation, in particular bulk synchronous processing. The end of Dennard-scaling and subsequent increases in CPU core counts each successive generation of general purpose processor has made the ability to leverage parallelism for communication an increasingly critical aspect for future extreme-scale application performance. But the use of massive multithreading in combination with MPI is an open research area, with many proposed approaches requiring code changes that can be unfeasible for important large legacy applications already written in MPI. This paper covers the design and initial evaluation of an extension of a massive multithreading runtime system supporting dynamic parallelism to interface with MPI to handle fine-grain parallel communication and communication-computation overlap. Our initial evaluation of the approach uses the ubiquitous stencil computation, in three dimensions, with the halo exchange as the driving example that has a demonstrated tie to real code bases. The preliminary results suggest that even for a very well-studied and balanced workload and message exchange pattern, co-scheduling work and communication tasks is effective at significant levels of decomposition using up to 131,072 cores. Furthermore, we demonstrate useful communication-computation overlap when handling blocking send and receive calls, and show evidence suggesting that we can decrease the burstiness of network traffic, with a corresponding decrease in the rate of stalls (congestion) seen on the host link and network.