A Hierarchical Approach for Load Balancing on Parallel Multi-core Systems

2013 42nd International Conference on Parallel Processing

Méhaut³

2013

Self Cite

The actor model is present in several missioncritical systems, such as those supporting WhatsApp and Twitter. These systems serve thousands of clients simultaneously, 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 performing 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 loadbalancing. 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 benchmarks 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.

Section: Related Workmentioning

confidence: 98%

A NUMA-Aware Runtime Environment for the Actor Model

Francesquini¹,

2013 42nd International Conference on Parallel Processing

Méhaut³

2013

Self Cite

“…Rashti [23] et al show how a better match between the communication and physical topologies on MPI applications can bring considerable gains in communication performance. Charm++ on NUMA machines face many of the same problems of actor-based REs and it has been shown that [20] NUMA awareness might bring improvements on the overall system performance.…”

Section: Related Workmentioning

confidence: 98%

Actor scheduling for multicore hierarchical memory platforms

Francesquini

Proceedings of the Twelfth ACM SIGPLAN Workshop on Erlang

Méhaut

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. Hierarchical 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 virtual machine (VM) for these platforms is quite limited. The current 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 NUMAaware improvements to the Erlang VM. This modified VM uses the NUMA characteristics and the application knowledge to take better memory management, scheduling and load-balancing decisions. We show that, when we consider the default Erlang VM as the baseline, 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.

“…Rashti [14] et al show how a better match between the communication and physical topologies on MPI applications can bring considerable gains in communication performance. Charm++ on NUMA machines face many of the same problems of actor REs and it has been shown that [11] NUMA awareness might bring improvements on the overall system performance. Aubry et al [16] demonstrate a complete dataflow based RE for the MPPA-256 architecture.…”

Section: Related Workmentioning

confidence: 98%

Improving the performance of actor model runtime environments on multicore and manycore platforms

Francesquini

Proceedings of the 2013 Workshop on Programming Based on Actors, Agents, and Decentralized Control

Méhaut

2013

The actor model is present in many systems that demand substantial computing resources which are often provided by multicore and multiprocessor platforms such as non-uniform memory access architectures (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 proposal to improve the performance of these systems. Using knowledge 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 adaptation to the Kalray MPPA-256 manycore processor.