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Palette: enabling scalable analytics for big-memory, multicore machines.
| Content Provider | CiteSeerX |
|---|---|
| Author | Chen, Fei Gonzalez, Tere Li, Jun Marwah, Manish Pruyne, Jim Viswanathan, Krishnamurthy Kim, Mijung |
| Abstract | Hadoop and its variants have been widely used for processing large scale analytics tasks in a cluster environment. However, use of a commodity cluster for analytics tasks needs to be reconsidered based on two key observations: (1) in recent years, large memory, multicore machines have become more affordable; and (2) recent studies show that most analytics tasks in practice are smaller than 100 GB. Thus, replacing a commodity cluster with a large memory, multicore machine can enable in-memory analytics at an affordable cost. However programming on a big-memory, multicore machine is a challenge. Multi-threaded programming is notoriously difficult. Further, the memory design of most large memory servers follows non-uniform memory access (NUMA) architecture. While NUMA-aware programming often leads to |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Multicore Machine Enabling Scalable Analytics Commodity Cluster Large Memory Recent Study Large Memory Server Affordable Cost Recent Year Cluster Environment Numa-aware Programming Key Observation Analytics Task Multi-threaded Programming Large Scale Analytics Task Non-uniform Memory Access In-memory Analytics Analytics Task Need Memory Design |
| Content Type | Text |
