Analyzing energy/performance trade-offs with power capping for parallel applications on modern multi and many core processors

Abstract

In the paper we present extensive results from analyzing energy/performance trade-offs with power capping observed on four different modern CPUs, for three different parallel applications such as 2D heat distribution, numerical integration and Fast Fourier Transform. The CPU tested represent both multi-core type CPUs such as Intel R Xeon R E5, desktop and mobile i7 as well as many-core Intel R Xeon Phi TM x200 but also server, desktop and mobile solutions used widely nowadays. We show that using enforced power caps we can find points of lower than default energy consumption but mostly for desktop and mobile solutions at the cost of increased execution time. We show with particular numbers how energy consumed, power consumption and execution time change for the point of minimum energy used versus the default configuration with no power limit, for each application and each tested CPU.

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

“…(3) Power capping [24] Using Intel RAPL for power management [40] Using Intel RAPL for analyzing energy/performance trade-offs with power capping for parallel applications on modern multi-and manycore processors [42] Using PAPI and Intel RAPL [62] Using Intel RAPL [46] Using Intel's power governor tool and Intel RAPL…”

“…Finally, applications and benchmarks used for power/ energy aware optimization in HPC systems are summarized in Table 7. It can be seen that NAS Parallel Benchmarks, [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…”

“…(1) Single/multicore/manycore CPU [33] A platform based on ARM Cortex A9, 4, 8, and 16 core architectures [49] Multicore CPUs as part of a node and cluster on which an MPI application runs [35] A chip with k cores with specific frequencies is considered [54] Cluster, 40 performance counters are investigated and correlated for energy-aware optimization, related to runtime, system, CPU, and memory power [38] A multicore system with cores as discrete thermal elements [58] Possibly also (2 multiprocessor system) [24] 32-node cluster each with 2 Sandy Bridge 8 core CPUs [40] Multicore and manycore CPUs [52] Sandy Bridge and Haswell Xeon CPUs [77] Servers with single CPUs hosting VMs [41] Single-core Pentium-M (32-bit) in a off-the-shelf laptop [59] Single-core AMD Athlon-64 [42] Intel Xeon Phi KNL 7250 processor with 68 cores, flat memory mode [43] Multicore Intel Xeon CPU…”

“…[37] Minimization of energy after an application has finished through frequency control [40] Energy minimization at the cost of increased execution time through power capping for parallel applications on modern multi and manycore processors [41] Energy minimization with low-performance degradation (aiming up to 5%) [61] Energy minimization of MPI programs through frequency scaling with constraints on execution time, linear programming approach is used, and traces from MPI application execution are collected (5) Product of energy and execution time [36] Finding an optimal GPU configuration (in terms of the number of threads per block and the number of blocks) Table 5: Energy/power control method.…”

Survey of Methodologies, Approaches, and Challenges in Parallel Programming Using High-Performance Computing Systems

“…Energy-aware HPC is one of the current trends that can be observed in both hardware development as well as software solutions, both at the scheduling and application levels [58]. When investigating performance vs energy consumption tradeoffs, it is possible to find nonobvious (i.e., nondefault) configurations using power capping (i.e., other than the default power limit) for both multicore CPUs [59] as well as GPUs [60]. However, optimal configurations can be very different, depending on both the CPU/GPU types as well as application profiles.…”