Cache Partitioning + Loop Tiling: A Methodology for Effective Shared Cache Management

Abstract

Abstract-In this paper, we present a new methodology that provides i) a theoretical analysis of the two most commonly used approaches for effective shared cache management (i.e., cache partitioning and loop tiling) and ii) a unified framework to fine tuning those two mechanisms in tandem (not separately). Our approach manages to lower the number of main memory accesses by one order of magnitude keeping at the same time the number of arithmetical/addressing instructions in a minimal level. We also present a search space exploration analysis where our proposal is able to offer a vast deduction in the required search space.

A methodology for efficient code optimizations and memory management

“…is method is applicable to all modern single-core and shared cache multi-core CPUs. Regarding shared cache processors, we use the so ware shared cache partitioning method given in our previous work [8]. No more than p threads can run in parallel (one to each core), where p is the number of the processing cores (single threaded codes only).…”

“…T pe1 L2acc. = arra size × ti + o f f set (8) where arra size is the size of the array and o f f set gives the number of L2 accesses of the new loop kernel added in the case the data array layout is transformed. t i gives how many times the corresponding array is accessed from L2 memory and is given by Eq.…”

“…In the case that the target metric is not ET or E, but the minimum number of L i memory accesses, then Algorithm 1 is changed accordingly, i.e., steps (1,2,5,8), (1,3,5,8) or (1,4,5,8) are executed only, respectively. It is important to note that in this case the number of di erent schedules that have to be further processed by Subsection 3.2 is smaller, i.e., the lower bound values of Eq.…”

Cache optimization cloud scheduling (COCS) algorithm based on last level caches

“…Therefore, a widespread literature survey is introduced on proper utilization of storage sub-systems and energy aware scheduling algorithms and their link with in a multi-core heterogeneous cloud computing environment. In [11], an algorithm for the efficient management of shared caches and their effective partitioning is presented to reduce the accessing of main memory in cloud computing environment. This technique helps to minimize the arithmetic and addressing operations.…”

“…Various researchers have introduced different cache memory Optimization techniques in above literatures. However, very few methods can be utilized in real-time due to various problems like high overhead, high energy consumption, slower performance and unable to reduce cache memory [11,12,14,[17][18][19]. Thus, we have adopted a Cache Optimization Cloud Scheduling ( ) Algorithm Based on Last Level Caches to ensure high cache memory Optimization and to enhance the processing speed of I/O subsystem in a cloud computing environment based on Dynamic voltage and Frequency Scaling ( ) technique.…”