Consolidation of different workloads often involves the use of virtualization technologies and/or partitioning of a physical server platform. Scenarios that...
Consolidation of different workloads often involves the use of virtualization technologies and/or partitioning of a physical server platform. Scenarios that involve partitioning sometimes result in under utilized hardware because of a too coarse granularity in compute resources. A single core might already be much more than the workload running on it needs. The same issue only seldom arises in memory assignments, the majority of partitioning technologies allow memory...
Consolidation of different workloads often involves the use of virtualization technologies and/or partitioning of a physical server platform. Scenarios that involve partitioning sometimes result in...
Some time ago I published an article on monitoring DAX activity on SPARC M8 (and to a certain degree the whole article also applies to SPARC M7 or S7) This...
Some time ago I published an article on monitoring DAX activity on SPARC M8 (and to a certain degree the whole article also applies to SPARC M7 or S7) This article better had preceded the article on the DAX as it targets the SPARC M8 core. The goal is to give you a better understanding of the plethora of performance relevant events that a SPARC M8 CPU can measure or count. Like all SPARC CPUs (and Intel CPU have similar event counters) SPARC M8 provides a lot of information...
Some time ago I published an article on monitoring DAX activity on SPARC M8 (and to a certain degree the whole article also applies to SPARC M7 or S7) This article better had preceded the article on...
A lot has already been published on the Software in Silicon innovations in the recent SPARC M8, S7 and M7 CPUs. A large part of these innovations are...
A lot has already been published on the Software in Silicon innovations in the recent SPARC M8, S7 and M7 CPUs. A large part of these innovations are implemented by a small dedicated unit on these CPUs called the DAX. The DAX itself can accelerate four basic operations, namely "scan", search for occurrences of a certain bit pattern in memory "select", pick chunks of data from a memory area "translate", apply a simple translation table to a region in memory (used in...
A lot has already been published on the Software in Silicon innovations in the recent SPARC M8, S7 and M7 CPUs. A large part of these innovations are implemented by a small dedicated unit on these...
One of the higher level goals of Spark MLlib should be to improve the efficiency of the ML algorithms that already exist. Currently ML has a reasonable coverage...
One of the higher level goals of Spark MLlib should be to improve the efficiency of the ML algorithms that already exist. Currently ML has a reasonable coverage of the important core algorithms. The work to get to feature parity for DataFrame-based API and model persistence are important. Apache Spark needs to use higher-level BLAS3 and LAPACK routines, instead of BLAS1 & BLAS2. For a long time we've used the concept of compute intensity (compute_intensity =...
One of the higher level goals of Spark MLlib should be to improve the efficiency of the ML algorithms that already exist. Currently ML has a reasonable coverage of the important core algorithms....
The explosive growth of data and the opportunity to discover insights from that data have never been greater, but the performance challenges of these massive...
The explosive growth of data and the opportunity to discover insights from that data have never been greater, but the performance challenges of these massive calculations can be daunting. Apache Spark SQL provides a powerful way for data scientist to easily process lots of data quickly. Apache Spark is rapidly evolving. Spark SQL and the new abstractions of Datasets/DataFrames provides a more expressive and powerful way to write code than previously possible with Spark's...
The explosive growth of data and the opportunity to discover insights from that data have never been greater, but the performance challenges of these massive calculations can be daunting. Apache Spark...
Processing lots of data with Java can require significant computing power. Oracle engineers have taken a two prong approach to improving Java performance....
Processing lots of data with Java can require significant computing power. Oracle engineers have taken a two prong approach to improving Java performance. First, on general Java performance, Oracle's SPARC M7 and SPARC S7 processors provide up to a 1.5x performance per core advantage over x86 cores as shown by these benchmark results "SPECjbb2015: SPARC T7-1 World Record for 1 Chip Result" and "SPECjbb2015: SPARC S7-2 Multi-JVM and Distributed Results". This was achieved by...
Processing lots of data with Java can require significant computing power. Oracle engineers have taken a two prong approach to improving Java performance. First, on general Java performance, Oracle's...
Please have a look at John's Blog if you are interested in SPEC performance and how people use it in different ways to come to different conclusions.
