A Novel Approach for Job Scheduling Optimizations Under Power Cap for ARM and Intel HPC Systems

Energy-Aware High-Performance Computing: Survey of State-of-the-Art Tools, Techniques, and Environments

“…(3) Cluster [47] Proposes integration of power limitation into a job scheduler and implementation in SLURM [48] Proposes the enhanced power adaptive scheduling (E-PAS) algorithm with integration of power-aware approach into SLURM for limiting power consumption [49] Approach applicable to MPI applications but focusing on states of processes running on CPUs, i.e., reducing power consumption of CPUs on which processes are idle or perform I/O operations [50] Proposes DVFS-aware profiling that uses design time profiling and nonprofiling approach that performs computations at runtime [51] Split compilation is used with offline and online phases, results from the offline-phase passed to runtime optimization, grey box approach to autotuning, and assumes code annotations [52] Proposes a runtime library that performs poweraware optimization at runtime and searches for good configurations with DFS/DCT for application regions [53] Approaches for modeling, monitoring, and tracking HPC systems using performance counters and optimization of energy used in a cluster environment with consideration of CPU, memory, disk, and network [54] Proposed an energy-saving framework with ranking and correlating counters important for improving energy efficiency [55,56] Energy savings on a cluster with Sandy Bridge processors [57] With consideration of disk and network scaling [58] Including disk, memory, processor, or even fans [24] Analysis of performance vs power of a 32-node cluster running a NAS parallel benchmark [59] A procedure for a single device (a compute node with CPU); however, it is dedicated using such devices coupled into a cluster (tested on 8-9 nodes) [60] Homogeneous multicore cluster [61] Cluster [62] Computer system with several nodes each with multicore CPUs [63,64] Cluster with several nodes each with multicore CPUs [65] Cluster with several nodes with CPUs [66,67] Cluster in a data center [68] Sandy Bridge cluster [69] Cluster with InfiniBand [70] Overprovisioned cluster which can run a certain number of nodes at peak power and more at lower power caps [71] Cluster with 1056 Dell PowerEdge SC1425 nodes …”

“…(2) Multiprocessor system [44] A heterogeneous real-time multiprocessor system-on-chip (MPSoC) system-consists of a number of processors each of which runs at its voltage and speed [47] A cluster with Intel Xeon CPUs [30] A multiprocessor system with Intel Xeon CPUs [48] A cluster with ARM CPUs, a cluster with Intel Ivy Bridge CPUs [74] A grid system parametrized with the number of hosts, distribution of computing capacities, and host selection policy [50] A system with a number of nodes with multicore CPUs assumed in the simulated HPC platform and cores of an Intel core M CPU with 6 voltage/frequency levels assumed [53] Cluster with consideration of CPU, memory, disk, and network [55,56,61,62,65] Cluster with CPUs [45] Many cores within a system, and core and uncore frequencies are of interest [57] With consideration of disk and network scaling [14] Systems with 2 socket Westmere-EP, 2 socket Sandy Bridge-EP, and 1 socket Ivy Bridge-HE CPUs [76] Undefined machines in a data center capable of hosting up to 15 VMs [60] Homogeneous multicore cluster [63,64] Cluster with multicore CPUs [66,67] Cluster in a data center [68] Sandy Bridge cluster [69] Cluster with InfiniBand [46] Dual-socket server with two Intel Xeon CPUs [70] Overprovisioned HPC cluster with CPUs [71] Cluster with 1056 Dell PowerEdge SC1425 nodes…”

“…SimGrid [85] is a discrete-event simulation framework for grid environments focusing on versatility and scalability. e tool supports three different sources of the input data: two [54] eoretical consideration of optimizations of an application that results in improvement of performance countervalues [36] Finding an optimal GPU configuration (in terms of the number of threads per block and the number of blocks) [53,57] Control of CPU frequency, spinning down the disk, and network speed scaling [43] Exploration of various loop scheduling ways, chunk sizes, optimization levels, and thread counts (5) Hybrid [30] Software + RAPL, the proposed PUPiL approach combines hardware's fast reaction time with flexibility of a software approach [48] Scheduling/software + resource management (including RAPL), the proposed algorithm takes into account real power and energy consumption [34] Concurrent kernel execution + DVFS [50,74] Scheduling + DVFS [38] Scheduling + DVS for minimization of temperature and meeting task deadlines [51] Scheduling jobs and management of resources and DVFS [77] Selection of the resources for a given user request, with VM migration and putting unused machines in the sleep mode [60] Workload distribution + DVFS-based multiobjective optimization [69] Polling, interrupt-driven execution (relinquishing CPU and waiting on a network event), DVFS power levers [70] Selection of nodes in an overprovisioned HPC clusters and Intel RAPL…”

“…Applications/benchmarks [33] Mibench and mediabench [44] Automotive-industrial, consumer-networking, telecom, mpeg [73] MD5 password breaking application [47] Monte Carlo simulation of particle transport unstructured implicit finite element method molecular dynamics unstructured shock hydrodynamics [30] Both single-application and multiapplication workloads PARSEC (x264, swaptions, vips, fluidanimate, Black Scholes, bodytrack), Minebench (ScalParC, kmeans, HOP, PLSA, svmfe, btree, kmeans_fuzzy), Rodinia (cfd, nn, lud, particlefilter), Jacobi, swish++, dijkstra [48] CoMD, Lulesh, MP2C [34] NAS parallel benchmarks (NPB) kernel EP, European option pricing benchmark Black Scholes [74] Workload simulated in a scheduling simulator [49] Matrix multiplication + P_write_priv benchmark-an I/O benchmark of IMB package [50] Job models from historical data from HLRS (highperformance computing center Stuttgart) [51] Early evaluation through an application extracted from Drug Discovery code, computation of interatomic distances and overlap of drug molecule, and protein active site probes [52] HPCG, NAS parallel benchmarks, NICAM-DC-MINI (for Post-K Japanese national flagship supercomputer development) [53] For system adaptation experiments: Molecular dynamics simulation (MDS), advance research Weather research and forecasting (WRF-ARW) [35] miniMD (parallel molecular dynamics code), Jacobi-3D stencil computation code [54] Parallel aerospace application PMLB (Lattice Boltzmann), parallel earthquake simulation eq3dyna [36] GEM software-calculation of electrostatic potentials generated by charges within molecules [75] Generation of parity data using GPUs [56] MILC, SWEEP3D, CG, FT, MG, LAMMPS, POP [55] MILC, GTC, SWEEP3D, PSCYEE, LBMHD, NAS parallel benchmarks-intratask scaling, LAMMPS, HYCOM, WRF, POP-intertask scaling [37] Selected MAGMA and Rodinia benchmarks [38] Synthetic sporadic real-time tasks used for evaluation of performance [45] Mega lattice Site updates per Second [39] Virusdetectioncl, NVIDIA CUDA SDK, GP GPU-Sim, Rodinia [57] IOzone, iperf, stream, stress (single node), a set of NPB benchmark (cluster): CG, MG, MDS, WRF-ARW, POP X1 benchmark, GeneHunter [14] NAS OpenMP parallel benchmarks…”

Performance and Power Analysis of HPC Workloads on Heterogeneous Multi-Node Clusters

“…Another promising approach towards energy-efficiency in HPC is the one of the EEHPCWG of N. Bates [13], trying to push an awareness action at the data center level. Part of this effort is the approach of optimizing the job scheduling using different power-aware policies presented by D. Tafani et al [14] or adding hardware/software extensions for improving energy awareness as presented by W. A. Ahmad et al [15]. Various attempts to take advantage of mobile technology for increasing energy efficiency of HPC systems have also been taken in the recent past.…”