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.
Mitigating Prefetcher-Caused Pollution using Informed Caching Policies for Prefetched Blocks
ACM Transactions on Architecture and Code Optimization (TACO), Vol. 11, No. 4, January 2015.
Vivek Seshadri, Samihan Yedkar, Hongyi Xin, Onur Mutlu, Phillip P. Gibbons*, Michael A. Kozuch*, Todd C. Mowry
Carnegie Mellon University
Many modern high-performance processors prefetch blocks into the on-chip cache. Prefetched blocks can potentially pollute the cache by evicting more useful blocks. In this work, we observe that both accurate and inaccurate prefetches lead to cache pollution, and propose a comprehensive mechanism to mitigate prefetcher-caused cache pollution.
First, we observe that over 95% of useful prefetches in a wide variety of applications are not reused after the first demand hit (in secondary caches). Based on this observation, our first mechanism simply demotes a prefetched block to the lowest priority on a demand hit. Second, to address pollution caused by inaccurate prefetches, we propose a self-tuning prefetch accuracy predictor to predict if a prefetch is accurate or inaccurate. Only predicted-accurate prefetches are inserted into the cache with a high priority.
Evaluations show that our final mechanism, which combines these two ideas, significantly improves performance compared to both the baseline LRU policy and two state-of-the-art approaches to mitigating prefetcher-caused cache pollution (up to 49%, and 6% on average for 157 two-core multiprogrammed workloads). The performance improvement is consistent across a wide variety of system configurations.
FULL PAPER: pdf