[databricks] Project Tungsten: Bringing Spark Closer to Bare Metal #spark #machinelearning

</div></div><p><strong>BigDataFr recommends: <a title="@databricks.com - Reynold Xin and Josen Rosen - Project Tungsten: Bringing Spark Closer to Bare Metal" href="https://databricks.com/blog/2015/04/28/project-tungsten-bringing-spark-closer-to-bare-metal.html?imm_mid=0d16f4&cmp=em-data-na-na-newsltr_20150506" target="_blank">Project Tungsten: Bringing Spark Closer to Bare Metal

« In a previous blog post, we looked back and surveyed performance improvements made to Spark in the past year. In this post, we look forward and share with you the next chapter, which we are calling Project Tungsten. 2014 witnessed Spark setting the world record in large-scale sorting and saw major improvements across the entire engine from Python to SQL to machine learning. Performance optimization, however, is a never ending process.

Project Tungsten will be the largest change to Spark’s execution engine since the project’s inception. It focuses on substantially improving the efficiency of memory and CPU for Spark applications, to push performance closer to the limits of modern hardware. This effort includes three initiatives:

-Memory Management and Binary Processing: leveraging application semantics to manage memory explicitly and eliminate the overhead of JVM object model and garbage collection
-Cache-aware computation: algorithms and data structures to exploit memory hierarchy
-Code generation: using code generation to exploit modern compilers and CPUs

The focus on CPU efficiency is motivated by the fact that Spark workloads are increasingly bottlenecked by CPU and memory use rather than IO and network communication. This trend is shown by recent research on the performance of big data workloads (Ousterhout et al) and we’ve arrived at similar findings as part of our ongoing tuning and optimization efforts for Databricks Cloud customers. »
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By Reynold Xin and Josh Rosen
Source: databricks.com

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