|
| Optimization level | Works | Description |
|
| (1) Single device | [33] | A platform based on ARM Cortex A9, 4, 8, and 16 core architectures |
| [34] | Scheduling kernels on a GPU and frequency scaling |
| [35] | A chip with k cores with specific frequencies is considered, and chips with 36 cores are simulated |
| [36] | Finding best application configuration and settings on a GPU |
| [37] | Server-type NVIDIA Tesla K20 m/K20c GPUs |
| [38] | Exploration of thermal-aware scheduling for tasks to minimize peak temperature in a multicore system through selection of core speeds |
| [39] | Comparison of energy/performance trade-offs for various GPUs |
| [40] | Server multicore and manycore CPUs, desktop CPU, mobile CPU |
| [41] | Single CPU under Linux kernel 2.6–11 |
| [42] | Intel Xeon Phi KNL 7250 computing platform, flat memory mode |
| [43] | Exploration of execution time and energy on a multicore Intel Xeon CPU |
|
| (2) Multiprocessor system | [44] | Task scheduling with thermal consideration for a heterogeneous real-time multiprocessor system-on-chip (MPSoC) system |
| [30] | Presents a hybrid approach PUPiL (Performance under Power Limits)—a hybrid software/hardware power capping system based on a decision framework going through nodes and making decisions on configuration, considered for single and multiapplication scenarios (cooperative and oblivious applications) |
| [45] | With notes specific to clusters |
| [14] | Systems with 2 socket Westmere-EP, 2 socket Sandy Bridge-EP, and 1 socket Ivy Bridge-HE CPUs |
| [46] | Dual-socket server with two Intel Xeon CPUs |
|
| (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 power-aware 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 |
| [72] | A cluster with 9421 servers connected by InfiniBand |
|
| (4) Grid | [73] | A cluster or collection of clusters allowed in the model and implementation |
| [74] | Implementations of hierarchical genetic strategy-based grid scheduler and algorithms evaluated against genetic algorithm variants |
|
| (5) Cloud | [75] | Meant for cloud storage systems |
| [76] | Related to assignment of applications to virtual and physical machines |
| [77] | Used as IaaS for computations |
|
