Ling Liu's SC13 paper "Large Graph Processing Without the Overhead" featured by HPCwire.
Another list highlighting Open Source Software Releases.
Second GraphLab workshop should be even bigger than the first! GraphLab is a new programming framework for graph-style data analytics.
The Application Slowdown Model: Quantifying and Controlling the Impact of Inter-Application Interference at Shared Caches and Main Memory
Proceedings of the 48th International Symposium on Microarchitecture (MICRO), Waikiki, Hawaii, USA, December 2015.
Lavanya Subramanian*† , Vivek Seshadri*, Arnab Ghosh*^, Samira Khan*‡,
*Carnegie Mellon University
† Intel Labs
^ IIT Kanpur
‡ University of Virginia
In a multi-core system, interference at shared resources (such as caches and main memory) slows down applications running on different cores. Accurately estimating the slowdown of each application has several benefits: e.g., it can enable shared resource allocation in a manner that avoids unfair application slowdowns or provides slowdown guarantees. Unfortunately, prior works on estimating slowdowns either lead to inaccurate estimates, do not take into account shared caches, or rely on a priori application knowledge. This severely limits their applicability.
In this work, we propose the Application Slowdown Model (ASM), a new technique that accurately estimates application slowdowns due to interference at both the shared cache and main memory, in the absence of a priori application knowledge. ASM is based on the observation that the performance of each application is strongly correlated with the rate at which the application accesses the shared cache. Thus, ASM reduces the problem of estimating slowdown to that of estimating the shared cache access rate of the application had it been run alone on the system. To estimate this for each application, ASM periodically 1) minimizes interference for the application at the main memory, 2) quantifies the interference the application receives at the shared cache, in an aggregate manner for a large set of requests. Our evaluations across 100 workloads show that ASM has an average slowdown estimation error of only 9.9%, a 2.97x improvement over the best previous mechanism.
We present several use cases of ASM that leverage its slowdown estimates to improve fairness, performance and provide slowdown guarantees. We provide detailed evaluations of three such use cases: slowdown-aware cache partitioning, slowdown-aware memory bandwidth partitioning and an example scheme to provide soft slowdown guarantees. Our evaluations show that these new schemes perform significantly better than state-of-the-art cache partitioning and memory scheduling schemes.
FULL PAPER: pdf