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.
Enabling Efficient and Scalable Hybrid Memories Using Fine-Granularity DRAM Cache Management
IEEE Computer Architecture Letters (CAL), March 2012.
Justin Meza*, Jichuan Chang†, HanBin Yoon*, Onur Mutlu*,
*Carnegie Mellon University
Hybrid main memories composed of DRAM as a cache to scalable non-volatile memories such as phase-change memory (PCM) can provide much larger storage capacity than traditional main memories. A key challenge for enabling high-performance and scalable hybrid memories, though, is efficiently managing the metadata (e.g., tags) for data cached in DRAM at a fine granularity. Based on the observation that storing metadata off-chip in the same row as their data exploits DRAM row buffer locality, this paper reduces the overhead of fine-granularity DRAM caches by only caching the metadata for recently accessed rows on-chip using a small buffer. Leveraging the flexibility and efficiency of such a fine-granularity DRAM cache, we also develop an adaptive policy to choose the best granularity when migrating data into DRAM. On a hybrid memory with a 512MB DRAM cache, our proposal using an 8KB on-chip buffer can achieve within 6% of the performance of, and 18% better energy efficiency than, a conventional 8MB SRAM metadata store, even when the energy overhead due to large SRAM metadata storage is not considered.
KEYWORDS: Cache memories, tag storage, non-volatile memories, hybrid main memories.
FULL PAPER: pdf