Fine-tune algorithms, learn how to vectorize confidently, and lots more
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The Parallel Universe Issue 36

 
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If you’re reading this, you’re most likely working in high-performance computing (HPC). If so, well played. HPC keeps growing at an annual rate of 3.5 percent1 and encompasses so many disciplines and industries, it’s exciting to think about what developers might accomplish next.

That’s why The Parallel Universe is here. The latest issue explores what more you can do with the latest Intel® architecture for the very best results. Effectively Train and Execute Machine Learning and Deep Learning Projects on CPUs shows how to take advantage of accelerated CPU training and inference execution—without making code changes. Boosting the Performance of Graph Analytics Workloads looks at ways to increase the efficiency of workload-heavy graph analytics applications on an Intel® Xeon® processor-based server.

With more cores available today, it’s worth asking How Effective Is Your Vectorization? The powerful Intel® Advisor tool can answer that, ensuring you’re getting the most out of your hardware. And if you’ve struggled with obtaining parallelism in Python*, you’re not alone. Parallelism in Python* Using Numba* jumps right into three ways to achieve performance with the popular open source framework.

All these and more articles are ready for you, one click away.

 
 

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Have ideas, input, or suggestions about what you’d like us to explain or explore in The Parallel Universe? We’d love to hear them.

 
 

1. https://superuser.openstack.org/articles/trends-high-performance-computing/.

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