I’m quite pleased with the Intel NUC boards that came with my surplus Rabbit cloud, but I can’t say the same about the Nvidia Jetson TK1 boards. They’re weird, flaky, limited, and haven’t proved useful even in simple applications. Frankly, I’m not sure what I’m going to do with the 15 Jetsons sitting in my office!
We live in a world of cattle, not pets, and Kubernetes rules the roost. I’ve been meaning to spend some time getting up to speed on the latest but didn’t have enough hardware to make that happen until now. I recently bought a whole pile of surplus hardware so I will be able to experiment with orchestration and container platforms in the office.
I’ve been thinking a lot lately about microprocessors, from the many-core CPUs that AMD and Intel introduced recently to the massively scalable GPGPU processing that’s taking machine learning by storm. After years of consolidation on commodity x86 CPUs, it seems that the computing paradigm is turning again to specialized offload processors. This trend towards heterogeneous computing will change the face of hardware, from mobile devices to the datacenter.
General-purpose GPU computing has been on the rise for years, from OpenCL and CUDA to machine learning and self-driving cars. But cryptocurrency mining has exploded in 2017, draining the market of AMD’s latest graphics cards as mining rigs pop up from basements to warehouses all over the world. The strength of Bitcoin in international finance suggests that Ethereum, Zcash, and others “altcoins” will find their own niches. We are seeing the emergence of a new computing category.
As I mentioned in my previous article, I decided to buy the 13″ Core i5 (base model) MacBook Pro. It meets my needs as a travel workstation, but how does it perform? I decided to benchmark it against my other Macs to see how it stands up.