In the era of Big Data and the growing support for Machine Learning, Deep Learning and Artificial Intelligence algorithms in the current software systems, there is an urgent need of standardized application benchmarks that stress test and evaluate these new technologies. Relying on the standardized BigBench (TPCx-BB) benchmark, this work enriches the improved BigBench V2 with three new workloads and expands the coverage of machine learning algorithms. Our workloads utilize multiple algorithms and compare different implementations for the same algorithm across several popular libraries like MLlib, SystemML, Scikit-learn and Pandas, demonstrating the relevance and usability of our benchmark extension.

Amazon Web Services (AWS) announced the launch of the third generation of its AWS Graviton chip-powered instances, the AWS Graviton3, will power all-new Amazon Elastic Compute 2 (EC2) C7g instances, which are currently available in preview, three years after the original version of the processors was released. According to AWS, the new Graviton3-powered instances will give up to 25% faster compute performance and 2x more excellent floating-point performance than the current generation of AWS EC2 C6g Graviton2-powered instances be unveiled at the AWS re:Invent 2021 conference in Las Vegas. According to AWS Graviton2 instances, the new Graviton3 instances are up to 2x quicker when performing cryptographic workloads compared to the business. According to AWS, the new Graviton3-powered instances will give up to 3x more excellent performance for machine learning workloads than Graviton2-powered instances, including support for bfloat16. The AWS Graviton chips are Arm-based 7nm processors custom-built for cloud workloads by Annapurna Labs, an Israeli engineering startup AWS bought roughly six years ago.

Oversimplified ML is "using data to answer questions." With traditional programming models, you create "rules" by using the programming language and apply these rules to the input to get output (results) (output). With ML training, you provide input and the output to train the program to create rules. This creates a predictive model that can be used to analyze previously unseen data to provide accurate answers. The key component of the entire ML process is data.

Performance of Machine Learning workloads using GPUs is by no means compromised when running on vSphere. In fact, you can often achieve better aggregate performance, i.e. throughput of many jobs, by running on vSphere vs. bare metal A key benefit of running GPU-based Machine Learning workloads on vSphere is the ability to allocate GPU resources in a very flexible and dynamic way. Performance of Machine Learning workloads using GPUs is by no means compromised when running on vSphere. In fact, you can often achieve better aggregate performance, i.e. throughput of many jobs, by running on vSphere vs. bare metal A key benefit of running GPU-based Machine Learning workloads on vSphere is the ability to allocate GPU resources in a very flexible and dynamic way.

Data is an important component in ML workloads and pipelines. Typically, data scientists and ML practitioners handle the data using existing primitives available through a scheduling system such as Kubernetes. However, the users still need to keep track of the data as well as the relationship between data and the primitives used. Data from multiple sources might be required to run their workloads and pipelines. In some cases (e.g., hyperparameter tuning), the data might need to be replicated or made available in some of the compute nodes in a cluster.

If IT were a television show, it would be "Hoarders." Organizations are creating and storing more and more data every day, and they're having a difficulty finding effective places to put it all. In fact, according to research by IDC, by 2020 we will hit the 44 zettabyte mark, with about 80 percent of the data not in databases. With such unprecedented data growth, IT teams are looking for flexible, scalable, easily manageable ways to preserve and protect that data. This is where object storage shines.