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Back-to-Basics Weekend Reading: Deep learning in neural networks

All Things Distributed

In the past few years, we have seen an explosion in the use of Deep Learning as its software platforms mature and the supporting hardware, especially GPUs with larger memories, become widely available. Jürgen Schmidhuber, in Neural Networks, Volume 61, January 2015, Pages 85-117 (DOI: 10.1016/j.neunet.2014.09.003). 2014.09.003).

Network 95
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Performance Testing with Open Source Tools – Myths and Reality

Alex Podelko

It was called Jellly at the time (mid 2015) but it was an early build of what OctoPerf would become. And when we started in 2015, JMeter was undoubtedly the best tool around. You have to remember this was in 2015, a lot has changed since, Gatling is definitely a stronger tool.

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USENIX LISA2021 Computing Performance: On the Horizon

Brendan Gregg

This was a chance to talk about other things I've been working on, such as the present and future of hardware performance. The video is on [youtube]: The slides are on [slideshare] or as a [PDF]: I work on many areas of performance, but recently I've had a lot of demand to talk about BPF.

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A Brief Guide of xPU for AI Accelerators

ACM Sigarch

HPU: Holographic Processing Unit (HPU) is the specific hardware of Microsoft’s Hololens. SPU: Stream Processing Unit (SPU) is related to the specialized hardware to process the data streams of video. TPU: Tensor Processing Unit (TPU) is Google’s specialized hardware for neural network.

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Safari 16.4 Is An Admission

Alex Russell

In leaner years (2012-2015), a single Fall release was all we'd get. From outright misstatements about a competitor's security, to claims that performance differences in hardware show Safari is faster, to [geographic brinksmanship](/2022/02/minimum-standards/), the confident bluster hasn't gone down particularly well.

Energy 93
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AWS EC2 Virtualization 2017: Introducing Nitro

Brendan Gregg

Hardware virtualization for cloud computing has come a long way, improving performance using technologies such as VT-x, SR-IOV, VT-d, NVMe, and APICv. The latest AWS hypervisor, Nitro, uses everything to provide a new hardware-assisted hypervisor that is easy to use and has near bare-metal performance. I'd expect between 0.1%

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The Return of the Frame Pointers

Brendan Gregg

2015-2020: Overhead As part of production rollout I did many performance overhead tests, which I've described publicly before: The overhead of adding frame pointers to everything (libc and Java) was usually less than 1%, with one exception of 10%. The actual overhead depends on your workload. Others have reported around 1% and around 2%.

Java 137