Many (core) Moore (Part III computing Epoch)

Back to the past – This is part III of four part story of the computing epochs as punctuated by Moore’s law in which Intel had its imprint for obvious reasons.

This is the 2003-2020 Era, in which multi-core, Open source, Virtualization, cloud infrastructure, social networks all blossomed…The onset of it was the end of MHz computing (Pentium IV) to multi-core and throughput computing.

It was also the beginning of my end in semiconductors for a brief period (20 years) until I decided its time to get back in the 2020s…That was punctuated by the first multi-core CPUs (mainstream) that Sun enabled – famously known as Niagara family and of-course the lesser know is UltraSPARC IIe which has an interesting contrast to Intel’s Banias (back to Pentium).

Some would call it Web2 era or Internet 2 era…The dot-com bubble which blew a number of companies in the prior era (OEM era), paved the way for new companies to emerge, thrive and establish the new stack. Notably at the infrastructure level, Moore was well ahead with first multi-core CPUs enabling virtualization and accelerated the decline of other processor companies (SPARC, MIPS), system OEMs as the market shifted from buying capital gear to cloud and opex.

Semiconductors investments started to go out of fashion as Intel dominated and other fabs (TI, National, Cypress, Philips, ST and many more withered) leaving Intel and TSMC with an also-ran Global foundries. In the same period, architectural consolidation around x86 happened along with Linux, ARM emerged as the alternative for. a new platform (mobile) via Apple. Looking back it was the value shifting from vertical integration (fab + processors) to SoC and thus IP (ARM) became dominant despite many attempts by processor companies to get into mobile.

Convergent to the emergence of iPhone/Apple/ARM, was AWS EC2 and S3 and thus the beginning of cloud with Opex as the new buying pattern instead of capex. This had significant implication as a decade later that very shift to commodity servers and opex comes full circle via Graviton and TPU with the cloud providers going vertical and investing in silicon. Intel’s lead on technology enabled x86 to dominate and when that lead in technology both slowed thanks to Moore’s law and TSMC, the shift towards vertical integration by the new system designers (Amazon, Google, Azure).

Simultaneously, emergence of ML as an emerging and significant workload that demanded new silicon types (GPU/TPU/MPU/DPU/xPU) and programming middleware (TensorFlow and PyTorch) broke the shackles from Unix/C/Linux to new frameworks and new hardware and software stack at the system level.

Nvidia happened to be at the right time at the right place (one can debate if GPU is the right architectural design), but certainly the new category or the tea leaves for the new system which is a CPU + xPU seeds were sown by mid 2010s….

All of the shift towards hyper scale distributed systems was fueled by Opensource. Some say that Amazon made all the money by reselling open source compute cycles. Quite true. Open source emerged and blossomed with the cloud and eventually the cloud would go vertical and raises the question – Is open source a viable investment strategy especially for infrastructure. The death of Sun microsystems was led by open source and. the purchase of RedHat by IBM formed the bookends of Open Source as the dominant investment thesis by the venture community. While open source is still viable and continues to thrive, it’s not front and center as a disruptor or primary investment thesis by end of this era as many more SaaS applications took the oxygen.

We started with 130nm 10 layers of metal with Intel taking the lead over TI and IBM and ended with 10nm from TSMC taking. the lead over Intel. How did that happen? Volumes have been written on Intel’s mis-steps, but clearly the investment into 3DXpoint and trying to innovate or bet with new materials and new devices to bridge the memory gap did not materialize and distracted. Good idea and important technology gap need, but picking the wrong material stack distracted.

The companies that emerged and changed the computing landscape were VMware, Open Source (many), Facebook, Apple (Mobile), China (as a geography ). The symbiotic relationship between VMware and Intel is best depicted in the chart below.

Single core to dual socket multi-core evolution…

On networking front The transition from 10Gbps to 100Gbps (10x) over the past decade is one of the biggest transformation of networking adoption of custom silicon design principles.

Above chart shows the flattening of the OEM business while the cloud made the pie larger. OEMs consolidated around big 6 (Dell, HPE, Cisco, Lenovo, NetApp, Arista) and rest withered.

GPU/xPU emerged as a category and along with resurgence in semiconductor investments (50+ startups with $2.5+B of venture dollars). Generalization of xPU with a dual heterogenous socket (CPU + xPU) is becoming the new building blocks for a system, thanks to CXL as well. The associated evolution and implications for the software layer was discussed here.

We conclude this era with the shift from 3-tier enterprise (‘modern mainframe’) stack that was serviced by OEMs to distrbuted systems as implemented by the cloud providers where use case (e-commerce, search, social) drove the system design whereas technology (Unix/C/RISC) drove the infrastructure design in the prior era (a note on that is coming…)

In summary – Moore’s law enabled multi-core, virtualization, distributed systems, but its slowdown of growth opened the gates for new systems innovation and thus new companies and new stack including significant headwinds for Intel.

Lets revisit some of the famous laws by famous people…

  1. Original Moore’s law – (cost, density)

Bill Joy’s change it to Performance Scaling. Certainly slowing down and shift in performance moved to throughput over latency. Needs update for ML/AI era, as it demands both latency and throughput.

2.Metcalfe’s Law – Still around. See the networking section.

3.Wrights Law (demand and volume) – – this predates moore’s law and now applies to many more domains – battery, biotech, solar etc…

4.Elon’s law – (A new one…) – Optimal alignment of atoms and how close to that is your error. We are approaching that.

5.Dennard Scaling – Power limits are being hit. Liquid cooling is coming down the cost curve rapidly.

wrong tool

You are finite. Zathras is finite. This is wrong tool.

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"knowledge speaks but wisdom listens" Jimi Hendrix.

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