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Actor Scheduling for Multicore Hierarchical Memory Platforms
- In Proceedings of the 12th ACM Erlang Workshop
, 2013
"... Erlang applications are present in several mission-critical systems. These systems demand substantial computing resources that are usually provided by multiprocessor and multi-core platforms. Hi-erarchical memory platforms, or Non-Uniform Memory Access (NUMA) architectures, account for an important ..."
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Erlang applications are present in several mission-critical systems. These systems demand substantial computing resources that are usually provided by multiprocessor and multi-core platforms. Hi-erarchical memory platforms, or Non-Uniform Memory Access (NUMA) architectures, account for an important share of these platforms. Yet, the research on the suitability of the current vir-tual machine (VM) for these platforms is quite limited. The cur-rent VM assumes a flat memory space, thus not performing as well as it could on these architectures. The NUMA environment presents challenges to the runtime environment in fields varying from memory management to scheduling and load-balancing. In this article we summarize some of the characteristics of an actor based application to, in light of the above, introduce some NUMA-aware improvements to the Erlang VM. This modified VM uses the NUMA characteristics and the application knowledge to take bet-ter memory management, scheduling and load-balancing decisions. We show that, when we consider the default Erlang VM as the base-line, the modified VM can achieve performance improvements up to a factor of 2.50 while limiting the slowdown on the worst case by a factor of 1.15.
A NUMA-Aware Runtime Environment for the Actor Model
- In Proceedings of the 42nd International Conference on Parallel Processing, ICPP 2013
, 2013
"... Abstract—The actor model is present in several mission-critical systems, such as those supporting WhatsApp and Twitter. These systems serve thousands of clients simultane-ously, therefore demanding substantial computing resources usually provided by multiprocessor and multicore platforms. Non-Unifor ..."
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Abstract—The actor model is present in several mission-critical systems, such as those supporting WhatsApp and Twitter. These systems serve thousands of clients simultane-ously, therefore demanding substantial computing resources usually provided by multiprocessor and multicore platforms. Non-Uniform Memory Access (NUMA) architectures account for an important share of these platforms. Yet, little or no research has been done on the suitability of the current actor runtime environments for these machines. Current runtime environments assume a flat memory space, thus not perform-ing as well as they could. The NUMA environment presents challenges to the actor model runtime environment in fields varying from memory management to scheduling and load-balancing. In this document we analyze and characterize actor based applications to, in light of the above, propose improvements to actor runtime environments. As a proof of concept, we have applied our ideas in a real actor runtime environment, the Erlang virtual machine. This modified virtual machine uses the NUMA characteristics and the application knowledge to take better memory management, scheduling and load-balancing decisions. We have evaluated this modified runtime environment using standard bench-marks and, taking the default virtual machine as a baseline, we improved the performance of the tested applications by a factor of 2.50 on the best case while limiting our slowdown on the worst case by a factor of 1.09. Keywords-actor model; NUMA; physical topology; Erlang; I.
THEME Distributed and High Performance
"... 3.2.1. Instrumentation, analysis and prediction tools 4 3.2.2. Fairness in large-scale distributed systems 4 3.2.3. Tools to operate clusters 4 3.2.4. Simple and scalable batch scheduler for clusters and grids 4 3.3. Migration and resilience; Large scale data management 5 ..."
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3.2.1. Instrumentation, analysis and prediction tools 4 3.2.2. Fairness in large-scale distributed systems 4 3.2.3. Tools to operate clusters 4 3.2.4. Simple and scalable batch scheduler for clusters and grids 4 3.3. Migration and resilience; Large scale data management 5
Improving the Performance of Actor Model Runtime Environments on Multicore and Manycore Platforms
"... The actor model is present in many systems that demand substan-tial computing resources which are often provided by multicore and multiprocessor platforms such as non-uniform memory access ar-chitectures (NUMA) and manycore processors. Yet, no mainstream actor model runtime environment (RE) currentl ..."
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The actor model is present in many systems that demand substan-tial computing resources which are often provided by multicore and multiprocessor platforms such as non-uniform memory access ar-chitectures (NUMA) and manycore processors. Yet, no mainstream actor model runtime environment (RE) currently in use takes into account the hierarchical memory characteristics of these platforms. These REs essentially assume a flat-memory space therefore not performing as well as they could. In this paper we present our pro-posal to improve the performance of these systems. Using knowl-edge about the general characteristics of actor-based applications and the underlying platform, we propose techniques spanning from memory management to scheduling and load-balancing. Based on previous work, we present our design guidelines for the RE adap-tation to the Kalray MPPA-256 manycore processor.
Pós-Graduação em Computação Topology-Aware Load Balancing for Performance Portability over Parallel High Performance Systems
"... Pour obtenir le grade de ..."
