New White Paper: High-Performance Virtualized Spark Clusters on Kubernetes for Deep Learning

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By Dave Jaffe, VMware Performance Engineering A new white paper is available showing the advantages of running virtualized Spark Deep Learning workloads on Kubernetes. Recent versions of Spark include support for Kubernetes. For Spark on Kubernetes, the Kubernetes scheduler provides the cluster manager capability provided by Yet Another Resource Negotiator (YARN) in typical Spark on Read more...

Sharing GPU for Machine Learning/Deep Learning on VMware vSphere with NVIDIA GRID: Why is it needed? And How to share GPU?

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By Lan Vu, Uday Kurkure, and Hari Sivaraman  Data scientists may use GPUs on vSphere that are dedicated to use by one virtual machine only for their modeling work, if they need to. Certain heavier machine learning workloads may well require that dedicated approach. However, there are also many ML workloads and user types that do not use Read more...

New white paper: Big Data performance on VMware Cloud on AWS: Spark machine learning and IoT analytics performance on-premises and in the cloud

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By Dave Jaffe A new white paper is available comparing Spark machine learning performance on an 8-server on-premises cluster vs. a similarly configured VMware Cloud on AWS cluster. Here is what the VMware Cloud on AWS cluster looked like: Three standard analytic programs from the Spark machine learning library (MLlib), K-means clustering, Logistic Regression classification, Read more...

Persistent Memory Performance in vSphere 6.7

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We published a paper that shows how VMware is helping advance PMEM technology by driving the virtualization enhancements in vSphere 6.7. The paper gives a detailed performance analysis of using PMEM technology on vSphere using various workloads and scenarios. These are the key points that we cover in this white paper: We explain how PMEM Read more...

Oracle Database Performance with VMware Cloud on AWS

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You’ve probably already heard about VMware Cloud on Amazon Web Services (VMC on AWS). It’s the same vSphere platform that has been running business critical applications for years, but now it’s available on Amazon’s cloud infrastructure. Following up on the many tests that we have done with Oracle databases on vSphere, I was able to Read more...

vCenter performance improvements from vSphere 6.5 to 6.7: What does 2x mean?

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In a recent blog, the VMware vSphere team shared the following performance improvements in vSphere 6.7 vs. 6.5: Moreover, with vSphere 6.7 vCSA delivers phenomenal performance improvements (all metrics compared at cluster scale limits, versus vSphere 6.5): 2X faster performance in vCenter operations per second 3X reduction in memory usage 3X faster DRS-related operations (e.g. Read more...

Performance Comparison of Containerized Machine Learning Applications Running Natively with Nvidia vGPUs vs. in a VM – Episode 4

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This article is by Hari Sivaraman, Uday Kurkure, and Lan Vu from the Performance Engineering team at VMware. Performance Comparison of Containerized Machine Learning Applications Docker containers [6] are rapidly becoming a popular environment in which to run different applications, including those in machine learning [1, 2, 3]. NVIDIA supports Docker containers with their own Docker engine Read more...

Episode 3: Performance Comparison of Native GPU to Virtualized GPU and Scalability of Virtualized GPUs for Machine Learning

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In our third episode of machine learning performance with vSphere 6.x, we look at the virtual GPU vs. the physical GPU. In addition, we extend the performance results of machine learning workloads using VMware DirectPath I/O (passthrough) vs. NVIDIA GRID vGPU that have been partially addressed in previous episodes: Episode 1: Performance Results of Machine Read more...