Speedup and Power Scaling Models for Heterogeneous Many-Core Systems

Abstract

Traditional speedup models, such as Amdahl's Law, Gustafson's, and Sun and Ni's models, have helped the research community and industry to better understand the performance capabilities of systems and the parallelizability of applications. Mostly targeting homogeneous hardware platforms or a limited form of processor heterogeneity, these models do not cover newly emerging multi-core heterogeneous architectures. This paper reports novel speedup and energy consumption models based on a more general representation of heterogeneity, called normal form heterogeneity, supporting a wide range of heterogeneous many-core architectures. The modelling method aims to predict system energy efficiency and performance ranges and facilitates research and development for the hardware and system software levels. Extensive experimentation on an off-the-shelf big.LITTLE heterogeneous platform validates the models showing less than 1% error for speedup and less than 4% error for power dissipation. The practical use of the method is demonstrated with a quantitative study of system load balancing efficiency.

Amdahl's law in the context of heterogeneous many‐core systems – a survey

“…The classical method for modelling the speedup of workload processing caused by some measure of improving the computation capabilities is known as Amdahl's law, which developed from observations presented by Amdahl in 1967 [1]. Amdahl [2,42] no p-fraction yes no no no no [3] no p-fraction yes yes yes yes no [5] load balancing and scheduling p-fraction yes yes yes yes no [7] no p-fraction yes yes yes no no [13] no parallelism yes yes no no no [14] load balancing and scheduling parallelism yes yes no no no [15] no p-fraction yes yes yes yes no [17] synchronisation and communication p-fraction yes yes no no no [18] no p-fraction yes no no no no [4,43] no p-fraction yes no no no no [19,20,44] no p-fraction yes no no no no [22] no p-fraction yes no no no no [24] no multi p-fraction and parallelism yes yes no no no [25] no p-fraction yes no no no no [26] time of parallel tasks no no no no no no [27] no p-fraction yes no no no no [28] no p-fraction yes no no no no [29] no no yes yes no no no [38] no [68] no p-fraction yes no no no no [69] run-time no no yes no no yes [70] run-time no no yes yes yes yes provide a mathematical formula for this law, which was later formulated based on his verbal arguments. Given the context of this paper, which is about the parallelisation of workloads on M/MCP systems, 'improvement of computation capabilities' generally means the incorporation of multiple processing units (to be called 'cores' in this paper) to improve the speed of workload execution, unless otherwise noted.…”

“…Summary: The Hill-Marty core heterogeneity assumptions are inappropriate for most modern heterogeneous multi-core architectures. To that end, a 'normal form' of HeMCP has emerged [5].…”

“…Parallelisation has been an essential method for improving the performance and energy efficiency of computation. On performance, it is well known that the parallelisation of workloads may bring speedup [1][2][3][4][5]. On energy efficiency, with the advent of such hardware techniques as dynamic voltage and frequency scaling (DVFS), it is possible to trade an increase in the number of processing units for a reduction of energy consumption without affecting performance [6][7][8][9].…”

“…Various problems related to mapping parallelisable (mostly software) workloads onto M/MCP hardware platforms have also been intensely scrutinised. The energy-efficient load balancing, task migration and scheduling have been the target of substantial investigations [5,[31][32][33][34].…”

Energy-Efficient Thread Mapping for Heterogeneous Many-Core Systems via Dynamically Adjusting the Thread Count

“…Regardless of a single processing core or multiple processing cores being executed, the SIP is fixed. On this basis, we propose a model by extending Amdahl's law in the multi-core era [19][20][21]. The model reflects the relationship of program performance and thread count.…”