Automatic Energy Saving Schemes for Parallel Applications

Although high-performance computing traditionally focuses on the efficient execution of large-scale applications, both energy and power have become critical concerns when approaching exascale. Drastic increases in the power consumption of supercomputers affect significantly their operating costs and failure rates. In modern microprocessor architectures, equipped with dynamic voltage and frequency scaling (DVFS) and CPU clock modulation (throttling), the power consumption may be controlled in software. Additionally, network interconnect, such as Infiniband, may be exploited to maximize energy savings while the application performance loss and frequency switching overheads must be carefully balanced. This work first studies two important collective communication operations, all-to-all and allgather and proposes energy saving strategies on the per-call basis. Next, it targets point-to-point communications to group them into phases and apply frequency scaling to them to save energy by exploiting the architectural and communication stalls. Finally, it proposes an automatic runtime system which combines both collective and point-to-point communications into phases, and applies throttling to them apart from DVFS to maximize energy savings. The experimental results are presented for NAS parallel benchmark problems as well as for the realistic parallel electronic structure calculations performed by the widely used quantum chemistry package GAMESS. Close to the maximum energy savings were obtained with a substantially low performance loss on the given platform

Distributed Strategy for Power Re-Allocation in High Performance Applications

To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to distribute a given power allocation among the cluster nodes assigned to the application while balancing their performance change. The strategy operates in a timeslice-based manner to estimate the current application performance and power usage per node followed by power redistribution across the nodes. Experiments, performed on four nodes (112 cores) of a modern computing platform interconnected with Infiniband showed that even a significant power budget reduction of 20% may result in a performance degradation of as low as 1% under the proposed strategy compared with the execution in the unlimited power cas

Runtime Energy Savings Based on Machine Learning Models for Multicore Applications

To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to maximize energy savings under a given performance degradation. Machine learning techniques were utilized to develop performance models which would provide accurate performance prediction with change in operating core-uncore frequency. Experiments, performed on a node (28 cores) of a modern computing platform showed significant energy savings of as much as 26% with performance degradation of as low as 5% under the proposed strategy compared with the execution in the unlimited power case

Runtime Power Allocation Based on Multi-GPU utilization in GAMESS

To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to maximize performance under a given power budget by distributing the available power according to the relative GPU utilization. Time series forecasting methods were used to develop workload prediction models that provide accurate prediction of GPU utilization during application execution. Experiments were performed on a multi-GPU computing platform DGX-1 equipped with eight NVIDIA V100 GPUs used for quantum chemistry calculations in the GAMESS package. For a limited power budget, the proposed strategy may deliver as much as hundred times better GAMESS performance than that obtained when the power is distributed equally among all the GPUs

Recent Developments in the General Atomic and Molecular Electronic Structure System

A discussion of many of the recently implemented features of GAMESS (General Atomic and Molecular Electronic Structure System) and LibCChem (the C++ CPU/GPU library associated with GAMESS) is presented. These features include fragmentation methods such as the fragment molecular orbital, effective fragment potential and effective fragment molecular orbital methods, hybrid MPI/OpenMP approaches to Hartree-Fock, and resolution of the identity second order perturbation theory. Many new coupled cluster theory methods have been implemented in GAMESS, as have multiple levels of density functional/tight binding theory. The role of accelerators, especially graphical processing units, is discussed in the context of the new features of LibCChem, as it is the associated problem of power consumption as the power of computers increases dramatically. The process by which a complex program suite such as GAMESS is maintained and developed is considered. Future developments are briefly summarized

Automatic Energy Saving Schemes for Parallel Applications

Although high-performance computing traditionally focuses on the efficient execution of large-scale applications, both energy and power have become critical concerns when approaching exascale. Drastic increases in the power consumption of supercomputers affect significantly their operating costs and failure rates. In modern microprocessor architectures, equipped with dynamic voltage and frequency scaling (DVFS) and CPU clock modulation (throttling), the power consumption may be controlled in software. Additionally, network interconnect, such as Infiniband, may be exploited to maximize energy savings while the application performance loss and frequency switching overheads must be carefully balanced. This work first studies two important collective communication operations, all-to-all and allgather and proposes energy saving strategies on the per-call basis. Next, it targets point-to-point communications to group them into phases and apply frequency scaling to them to save energy by exploiting the architectural and communication stalls. Finally, it proposes an automatic runtime system which combines both collective and point-to-point communications into phases, and applies throttling to them apart from DVFS to maximize energy savings. The experimental results are presented for NAS parallel benchmark problems as well as for the realistic parallel electronic structure calculations performed by the widely used quantum chemistry package GAMESS. Close to the maximum energy savings were obtained with a substantially low performance loss on the given platform.</p

Per-call Energy Saving Strategies in All-to-all Communications

With the increase in the peak performance of modern computing platforms, their energy consumption grows as well, which may lead to overwhelming operating costs and failure rates. Techniques, such as Dynamic Voltage and Frequency Scaling (called DVFS) and CPU Clock Modulation (called throttling) are often used to reduce the power consumption of the compute nodes. However, these techniques should be used judiciously during the application execution to avoid significant performance losses. In this work, two implementations of the all-to-all collective operations are studied as to their augmentation with energy saving strategies on the per-call basis. Experiments were performed on the OSU MPI benchmark as well as NAS and CPMD application benchmarks, in which power consumption was reduced by up to 10% and 15.7%, respectively, with little performance degradation.</p

Runtime Power-Aware Energy-Saving Scheme for Parallel Applications

Energy consumption has become a major design constraint in modern computing systems. With the advent of peta ops architectures, power efficient software stacks have become imperative for scalability. Modern processors provide techniques, such as dynamic voltage and frequency scaling (DVFS), to improve energy efficiency on-the-fly. Without careful application, however, DVFS and throttling may cause significant performance loss due to the system overhead. Typically, these techniques are used by constraining a priori the application performance loss, under which the energy savings are sought. This paper discusses potential drawbacks of such usage and proposes an energy-saving scheme that takes into account the instantaneous processor power consumption as presented by the running average power limit" (RAPL) technology from Intel. Thus, the need for the user to define a performance loss tolerance apriori is avoided. Experiments, performed on NAS benchmarks, show that the proposed scheme saves more energy than the approaches based on the pre-defined performance loss.</p