Cloud expansion, part 2nd: Viewing the progress of the design of high-performance cloud systems

Learn how to leverage collaborative processing, non-volatile memory, interconnect, and storage

Be aware of the potential benefits of narrowing the access gap or using the coprocessor to advance the process to the I/O path, and breaking device technology requires the system designer to rethink how to design the application software. Explore and consider how the latest memory, computing devices, interconnected devices, and subsystems can affect your scalable, data-centric, high-performance cloud computing system devices. Breakthroughs in device technology are used to transform between "compute-centric" and more balanced "data-centric" computing infrastructures.

The author investigates storage-level memory, demonstrates how to populate the long-standing performance gap between RAM and rotating disk storage, and details the use of I/O bus coprocessor (which handles similar data), and explains how to build a low-cost high-performance interconnect network using InfiniBand, and discusses the extensible storage of unstructured data.

Computational systems engineering has historically been controlled by an extended processor and a dynamic RAM (DRAM) interface for memory work, leaving a huge gap between data-driven and computational algorithms. The interest in data-centric computing is growing rapidly, along with novel system design software and hardware equipment to support data transformations in a large number of datasets.

Software-focused data is no doubt the current focus of applications, such as video analytics, sensor networks, social networks, computer vision and augmented reality, intelligent transportation, and machine-wide data initiatives for machine systems, such as IBM's intelligent planet and intelligent city.

At present, the focus is on collecting, processing, transforming, and mining large datasets:

In nonvolatile memory (storage-level memory, SCM), data focus tends to be a new device-level breakthrough, which makes large data more needed to be processed.

At the same time, the input/output coprocessor makes processing more prone to data.

Finally, low latency, high-bandwidth, off-the-shelf interconnects such as InfiniBand support researchers to quickly build 3D rings and fat tree clusters that can be used to limit the most bizarre and expensive custom high-performance computing (HPC) designs.

So far, system software, even system design, is still affected by outdated bottlenecks and ideas. For example, consider threading and multiple programming. The whole idea stems from slow disk drive access, and can the program do anything other than run another program while waiting for the data? Of course, we have redundant array of independent disks (RAID) extensions and NAND Flash solid-state disks (SSD), but as the IBM Almaden study shows, the time scale differences in access time gaps are huge in human languages.

For each device, the access time gap between CPU, RAM, and storage can be measured in a typical performance form, but perhaps the gap may be easier to understand in the case of human language (as the Institute of IBM Almaden for illustration).

If typical CPU operations resemble what humans do in a matter of seconds, a 100-fold RAM access delay may take several minutes to access information. However, over a similar comparison, 100-times delayed disk access is about a few months (100 days) compared to RAM. (see Figure 1.) ）

Figure 1. Data access gap

Many experienced computer engineers do not seriously think about 100 to 200 random I/O operations per second (IOPS): This is a disk-driven mechanical boundary. (Of course, sequential access can be as high as hundreds of megabytes per second, but random access is still similar to more than 50 years ago, with 15K RPM search and rotational access latency.) ）

Finally, as Almaden points out, the tape is extremely slow, just as slow as the glacier moves. So why are we still confused? Of course it's because of capacity. But what should we do with data or make data processing more efficient?

Let's look at figure 1 again. Improvements to NAND flash memory for mobile devices and more recent SSDs help narrow the gap; however, it is widely believed that NAND flash technology will soon reach its limits, as many system researchers have pointed out. The use of the transistor floating gate technology has reached the expansion limit, further expansion will lead to lower reliability, so, although this is a stopgap to use for data-centric computing, but this may not be the solution.

Conversely, several new non-volatile RAM (NVRAM) device technologies may be solutions that include:

Phase Change RAM (Pcram): This memory uses a heating element to turn a material called a sulfur compound into a crystalline or amorphous glass state, thus storing two programmable and read states that can be maintained even if they are not powered. For M class synchronous Non-volatile memory (NVM), Pcram seems to have honoured most of the recent commitments.

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