The Morning Paper

OCTOBER 4, 2020

Understanding operational 5G: a first measurement study on its coverage, performance and energy consumption , Xu et al., What is the end-to-end throughput and latency, and where are the bottlenecks? energy consumption). Throughput and latency. Application performance. SIGCOMM’20.

Energy 130

Energy Latency Performance Network 130

The Morning Paper

MAY 28, 2019

Boosted race trees for low energy classification Tzimpragos et al., We don’t talk about energy as often as we probably should on this blog, but it’s certainly true that our data centres and various IT systems consume an awful lot of it. One efficient way of doing that in analog hardware is the use of current-starved inverters.

Energy 52

Energy Hardware Efficiency Architecture 52

Scalegrid

FEBRUARY 8, 2024

Key Takeaways Distributed storage systems benefit organizations by enhancing data availability, fault tolerance, and system scalability, leading to cost savings from reduced hardware needs, energy consumption, and personnel. By implementing data replication strategies, distributed storage systems achieve greater.

Storage 130

Storage Systems Big Data Azure 130

ACM Sigarch

MAY 31, 2023

Heterogeneous and Composable Memory (HCM) offers a feasible solution for terabyte- or petabyte-scale systems, addressing the performance and efficiency demands of emerging big-data applications. CXL-based disaggregated memory provides a new opportunity with two orders of magnitude better performance than RDMA (as illustrated in Figure 2).

Latency 52

Latency Hardware Cache Architecture 52

The Morning Paper

NOVEMBER 15, 2020

This makes the whole system latency sensitive. So we need low latency, but we also need very high throughput: A recurring theme in IDS/IPS literature is the gap between the workloads they need to handle and the capabilities of existing hardware/software implementations. The target FPGA for Pigasus has 16MB of BRAM.

Servers 128

Servers Hardware Latency Design 128

HammerDB

OCTOBER 18, 2019

Nevertheless in this blog sometimes we do publish performance data to highlight best practices or potential configuration pitfalls and although we’ve mentioned this one before it is worth dedicating an entire post to it as this issue seems to appear numerous times running database workloads on Linux. hardware limits: 1000 MHz - 4.00

Benchmarking 63

Benchmarking Performance Hardware C++ 63

The Morning Paper

JUNE 13, 2019

As we saw with the SOAP paper last time out, even with a fixed model variant and hardware there are a lot of different ways to map a training workload over the available hardware. “ For instance, Facebook applications issue tens-of-trillions of inference queries per day with varying performance, accuracy, and cost constraints.”

Hardware 58

Hardware Latency Serverless Energy 58