Storage IO straight to GPU

The parallel processing power of the GPU (Graphics Processing Unit) cannot be denied. One year ago, nVidia® overtook Intel® in market capitalization. And today, they have doubled their market cap lead over Intel®,  [as of July 2, 2021] USD$510.53 billion vs USD$229.19 billion.

Thus it is not surprising that storage architectures are changing from the CPU-centric paradigm to take advantage of the burgeoning prowess of the GPU. And 2 announcements in the storage news in recent weeks have caught my attention – Windows 11 DirectStorage API and nVidia® Magnum IO GPUDirect® Storage.

nVidia GPU

Exciting the gamers

The Windows DirectStorage API feature is only available in Windows 11. It was announced as part of the Xbox® Velocity Architecture last year to take advantage of the high I/O capability of modern day NVMe SSDs. DirectStorage-enabled applications and games have several technologies such as D3D Direct3D decompression/compression algorithm designed for the GPU, and SFS Sampler Feedback Streaming that uses the previous rendered frame results to decide which higher resolution texture frames to be loaded into memory of the GPU and rendered for the real-time gaming experience.

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Is Software Defined right for Storage?

George Herbert Leigh Mallory, mountaineer extraordinaire, was once asked “Why did you want to climb Mount Everest?“, in which he replied “Because it’s there“. That retort demonstrated the indomitable human spirit and probably exemplified best the relationship between the human being’s desire to conquer the physical limits of nature. The software of humanity versus the hardware of the planet Earth.

Juxtaposing, similarities can be said between software and hardware in computer systems, in storage technology per se. In it, there are a few schools of thoughts when it comes to delivering storage services with the notable ones being the storage appliance model and the software-defined storage model.

There are arguments, of course. Some are genuinely partisan but many a times, these arguments come in the form of the flavour of the moment. I have experienced in my past companies touting the storage appliance model very strongly in the beginning, and only to be switching to a “software company” chorus years after that. That was what I meant about the “flavour of the moment”.

Software Defined Storage

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Open Source and Open Standards open the Future

[Disclosure: I was invited by GestaltIT as a delegate to their Storage Field Day 19 event from Jan 22-24, 2020 in the Silicon Valley USA. My expenses, travel, accommodation and conference fees were covered by GestaltIT, the organizer and I was not obligated to blog or promote the vendors’ technologies to be presented at this event. The content of this blog is of my own opinions and views]

Western Digital dived into Storage Field Day 19 in full force as they did in Storage Field Day 18. A series of high impact presentations, each curated for the diverse requirements of the audience. Several open source initiatives were shared, all open standards to address present inefficiencies and designed and developed for a greater future.

Zoned Storage

One of the initiatives is to increase the efficiencies around SMR and SSD zoning capabilities and removing the complexities and overlaps of both mediums. This is the Zoned Storage initiatives a technical working proposal to the existing NVMe standards. The resulting outcome will give applications in the user space more control on the placement of data blocks on zone aware devices and zoned SSDs, collectively as Zoned Block Device (ZBD). The implementation in the Linux user and kernel space is shown below:

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Data Renaissance in Oil and Gas

The Oil and Gas industry, especially in the upstream Exploration and Production (EP) sector, has been enjoying a renewed vigour in the past few years. I have kept in touch with the developments of the EP side because I always have a soft spot for the industry. I have engaged in infrastructure and solutions in the petrotechnical side in my days at Sun Microsystems back in the late 90s. The engagements with EP intensified in my first stint at NetApp, wearing the regional Oil & Gas consulting engineer here in South Asia for almost 6 years. Then, with Interica in 2014, I was dealing with subsurface data and seismic interpretation technology. EP is certainly an exciting sector to cover because there are so much technical work involved and the technologies, especially the non-IT, are breath taking.

I have been an annual registrant to the Digital Energy Journal events since 2013, except last year, and I have always enjoyed their newsletter. This week I attended Digital Energy 2-day conference again, and I was taken in by the exciting times in EP. Here are a few of my views and trends observation in this data renaissance.

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Scaling new HPC with Composable Architecture

[Disclosure: I was invited by Dell Technologies as a delegate to their Dell Technologies World 2019 Conference from Apr 29-May 1, 2019 in the Las Vegas USA. Tech Field Day Extra was an included activity as part of the Dell Technologies World. My expenses, travel, accommodation and conference fees were covered by Dell Technologies, the organizer and I was not obligated to blog or promote their technologies presented at this event. The content of this blog is of my own opinions and views]

Deep Learning, Neural Networks, Machine Learning and subsequently Artificial Intelligence (AI) are the new generation of applications and workloads to the commercial HPC systems. Different from the traditional, more scientific and engineering HPC workloads, I have written about the new dawn of supercomputing and the attractive posture of commercial HPC.

Don’t be idle

From the business perspective, the investment of HPC systems is high most of the time, and justifying it to the executives and the investors is not easy. Therefore, it is critical to keep feeding the HPC systems and significantly minimize the idle times for compute, GPUs, network and storage.

However, almost all HPC systems today are inflexible. Once assigned to a project, the resources pretty much stay with the project, even when the workload processing of the project is idle and waiting. Of course, we have to bear in mind that not all resources are fully abstracted, virtualized and software-defined whereby you can carve out pieces of the hardware and deliver a percentage of that resource. Case in point is the CPU, where you cannot assign certain clock cycles of CPU to one project and another half to the other. The technology isn’t there yet. Certain resources like GPU is going down the path of Virtual GPU, and into the realm of resource disaggregation. Eventually, all resources of the HPC systems – CPU, memory, FPGA, GPU, PCIe channels, NVMe paths, IOPS, bandwidth, burst buffers etc – should be disaggregated and pooled for disparate applications and workloads based on demands of usage, time and performance.

Hence we are beginning to see the disaggregated HPC systems resources composed and built up the meet the diverse mix and needs of HPC applications and workloads. This is even more acute when a AI project might grow cold, but the training of AL/ML/DL workloads continues to stay hot

Liqid the early leader in Composable Architecture

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