Energy-proportional designs would enable large energy savings in servers, potentially doubling their efficiency in real-life use. Achieving energy proportionality will require significant improvements in the energy usage profile of every system component, particularly the memory and disk subsystems.

/pdf/the-case-for-energy-proportional-computing-4un8a5n8c1.pdf

The Case for Energy-Proportional Computing

A system is disclosed comprising a processor, a trace module, a second buffer, and a bridge. The trace module has a first buffer configured to receive trace data from the processor at a first clock frequency. The second buffer is configured to receive trace data from the first buffer at a second clock frequency. The bridge is configured to receive trace data from the second buffer and output the trace data received from the second buffer at a third clock frequency. The second clock frequency is greater than the first clock frequency and the third clock frequency.

Virtual extension of buffer to reduce buffer overflow during tracing

A system includes a memory controller and a memory device having a command interface and a plurality of memory banks, each with a plurality of rows of memory cells. The memory controller transmits an auto-refresh command to the memory device. Responsive to the auto-refresh command, during a first time interval, the memory device performs refresh operations to refresh the memory cells and the command interface of the memory device is placed into a calibration mode for the duration of the first time interval. Concurrently, during at least a portion of the first time interval, the memory controller performs a calibration of the command interface of the memory device. The auto-refresh command may specify an order in which memory banks of the memory device are to be refreshed, such that the memory device sequentially refreshes a respective row in the plurality of memory banks in the specified bank order.

Maintenance operations in a DRAM

In an example, selected portions of an uncore fabric of a system-on-a-chip (SoC) or other embedded system is divided into two independent pipelines. Each pipeline operates independently of the other pipeline, and each accesses only one-half of the system memory, such as even or odd addresses in an interleaved memory. The two pipelines do not reconverge until after memory values have been returned. However, the uncore fabric may still present a single, monolithic interface to requesting devices. This allows system designers to treat the uncore fabric as a “black box” without modifying existing designs. Each incoming address may be processed by a deterministic hash, assigned to one of the pipelines, processed through memory, and then passed to a credit return.

Mitigating traffic steering inefficiencies in distributed uncore fabric

The present application is directed to a memory-access-multiplexing memory controller that can multiplex memory accesses from multiple hardware threads, cores, and processors according to externally specified policies or parameters, including policies or parameters set by management layers within a virtualized computer system A memory-access-multiplexing memory controller provides, at the physical-hardware level, a basis for ensuring rational and policy-driven sharing of the memory-access resource among multiple hardware threads, cores, and/or processors

Memory-access-resource management

In an embodiment, a system includes a memory controller, processors and corresponding caches. The system may include sources of uncertainty that prevent the precise scheduling of data forwarding for a load operation that misses in the processor caches. The memory controller may provide an early response that indicates that data should be provided in a subsequent clock cycle. An interface unit between the memory controller and the caches/processors may predict a delay from a currently-received early response to the corresponding data, and may speculatively prepare to forward the data assuming that it will be available as predicted. The interface unit may monitor the delays between the early response and the forwarding of the data, or at least the portion of the delay that may vary. Based on the measured delays, the interface unit may modify the subsequently predicted delays.

Critical word forwarding with adaptive prediction

In an embodiment, a memory controller includes multiple ports. Each port may be dedicated to a different type of traffic. In an embodiment, quality of service (QoS) parameters may be defined for the traffic types, and different traffic types may have different QoS parameter definitions. The memory controller may be configured to scheduled operations received on the different ports based on the QoS parameters. In an embodiment, the memory controller may support upgrade of the QoS parameters when subsequent operations are received that have higher QoS parameters, via sideband request, and/or via aging of operations. In an embodiment, the memory controller is configured to reduce emphasis on QoS parameters and increase emphasis on memory bandwidth optimization as operations flow through the memory controller pipeline.

Multi-Ported Memory Controller with Ports Associated with Traffic Classes

In one embodiment, an apparatus comprises an interconnect; at least one processor coupled to the interconnect; and at least one memory controller coupled to the interconnect. The memory controller is programmable by the processor into a loopback test mode of operation and, in the loopback test mode, the memory controller is configured to receive a first write operation from the processor over the interconnect. The memory controller is configured to route write data from the first write operation through a plurality of drivers and receivers connected to a plurality of data pins that are capable of connection to one or more memory modules. The memory controller is further configured to return the write data as read data on the interconnect for a first read operation received from the processor on the interconnect.

Memory controller with loopback test interface

Techniques are disclosed relating to determining when a data strobe signal is valid for capturing data. In one embodiment, an apparatus is disclosed that includes a memory interface circuit configured to determine an initial time value for capturing data from a memory based on a data strobe signal. In some embodiments, the memory interface circuit may determine this initial time value by reading a known value from memory. In one embodiment, the memory interface circuit further configured to determine an adjusted time value for capturing the data, where the memory interface circuit is configured to determine the adjusted time value by using the initial time value to sample the data strobe signal.

Dynamic data strobe detection

In an embodiment, a memory controller includes multiple ports. Each port may be dedicated to a different type of traffic. In an embodiment, quality of service (QoS) parameters may be defined for the traffic types, and different traffic types may have different QoS parameter definitions. The memory controller may be configured to schedule operations received on the different ports based on the QoS parameters. In an embodiment, the memory controller may support upgrade of the QoS parameters when subsequent operations are received that have higher QoS parameters, via sideband request, and/or via aging of operations. In an embodiment, the memory controller is configured to reduce emphasis on QoS parameters and increase emphasis on memory bandwidth optimization as operations flow through the memory controller pipeline.

Dynamic QoS Upgrading

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Hao Chen

Author Tools

Chat about Author