The central processing unit (CPU) and the graphics processing unit (GPU) handle different types of data. The CPU is used in almost all devices including
While the GPU complements the CPU, it handles complex workloads like supercomputing, AI, machine learning and extensive data analysis where numerical precision is required.
At the same time, companies are constantly searching for more efficient ways to cut costs and improve productivity at the chip level.
The Seattle- and Seoul-based startup says its DPU solution enables data centers to reduce power consumption and optimize performance with cost efficiency and security.
CEO of MangoBoost, Jangwoo Kim, says the company achieved efficiency through more than nine years of R&D about how the DPU technology works for data centers at the Seoul National University laboratory.
A host of companies provides DPU-like technology, including Microsoft FPGA Smart NIC and Amazon Nitro.
He added that one of the key differentiators of MangoBoost’s products is that it provides an extensive and customizable set of DPU features that can meet each customer’s needs.
The original article contains 592 words, the summary contains 146 words. Saved 75%. I’m a bot and I’m open source!
A data processing unit (DPU) is a programmable computer processor that tightly integrates a general-purpose CPU with network interface hardware.[1] Sometimes they are called “IPUs” (for “infrastructure processing unit”) or “SmartNICs”.[2] They can be used in place of traditional NICs to relieve the main CPU of complex networking responsibilities and other “infrastructural” duties; although their features vary, they may be used to perform encryption/decryption, serve as a firewall, handle TCP/IP, process HTTP requests, or even function as a hypervisor or storage controller.[1][3] These devices can be attractive to cloud computing providers whose servers might otherwise spend a significant amount of CPU time on these tasks, cutting into the cycles they can provide to guests.[1]
Wikipedia “DPU”
This does not seem to be something that specifically benefits AI workloads. Maybe they focused the marketing on that because of the AI hype?
This is the best summary I could come up with:
While the GPU complements the CPU, it handles complex workloads like supercomputing, AI, machine learning and extensive data analysis where numerical precision is required.
At the same time, companies are constantly searching for more efficient ways to cut costs and improve productivity at the chip level.
The Seattle- and Seoul-based startup says its DPU solution enables data centers to reduce power consumption and optimize performance with cost efficiency and security.
CEO of MangoBoost, Jangwoo Kim, says the company achieved efficiency through more than nine years of R&D about how the DPU technology works for data centers at the Seoul National University laboratory.
A host of companies provides DPU-like technology, including Microsoft FPGA Smart NIC and Amazon Nitro.
He added that one of the key differentiators of MangoBoost’s products is that it provides an extensive and customizable set of DPU features that can meet each customer’s needs.
The original article contains 592 words, the summary contains 146 words. Saved 75%. I’m a bot and I’m open source!
This does not seem to be something that specifically benefits AI workloads. Maybe they focused the marketing on that because of the AI hype?