Load and store instructions first generate an effective address, then perform address translation before accessing the data cache for load or store operations. For loads, the cache is read to return data, while stores write data to the cache. Stores are held in the store buffer until retirement to maintain load-store ordering. Loads can bypass and forward from earlier stores in the store buffer to improve performance. Memory dependencies between loads and stores are difficult to handle due to dynamic addresses and long memory latency. Speculative load disambiguation predicts dependencies to allow out-of-order execution when aliases are rare.
Join this video course on udemy . Click here :
https://www.udemy.com/course/mastering-microcontroller-with-peripheral-driver-development/?couponCode=SLIDESHARE
In this course, the code is developed such a way that, It can be ported to any MCU you have at your hand.
If you need any help in porting these codes to different MCUs you can always reach out to me!
The course is strictly not bound to any 1 type of MCU. So, if you already have any Development board which runs with ARM-Cortex M3/M4 processor,
then I recommend you to continue using it.
But if you don’t have any Development board, then check out the below Development boards.
Deadlock is a very important topic in operating system. In this presentation slide, try to relate deadlock with real life scenario and find out some solution with two main algorithm- Safety and Banker's Algorithm.
Join this video course on udemy . Click here :
https://www.udemy.com/course/mastering-microcontroller-with-peripheral-driver-development/?couponCode=SLIDESHARE
In this course, the code is developed such a way that, It can be ported to any MCU you have at your hand.
If you need any help in porting these codes to different MCUs you can always reach out to me!
The course is strictly not bound to any 1 type of MCU. So, if you already have any Development board which runs with ARM-Cortex M3/M4 processor,
then I recommend you to continue using it.
But if you don’t have any Development board, then check out the below Development boards.
Deadlock is a very important topic in operating system. In this presentation slide, try to relate deadlock with real life scenario and find out some solution with two main algorithm- Safety and Banker's Algorithm.
Operating system - Process and its conceptsKaran Thakkar
This presentation gives an overview of Process concepts in Operating System. The presentation aims at alleviating most of the overheads while understanding the process concept in operating system. this tailor made presentation will help individuals to understand the overall meaning of process and its underlying concepts used in an operating system.
Maximum CPU utilization obtained with multiprogramming
CPU–I/O Burst Cycle – Process execution consists of a cycle of CPU execution and I/O wait
CPU burst followed by I/O burst
CPU burst distribution is of main concern
Operating system 23 process synchronizationVaibhav Khanna
Processes can execute concurrently
May be interrupted at any time, partially completing execution
Concurrent access to shared data may result in data inconsistency
Maintaining data consistency requires mechanisms to ensure the orderly execution of cooperating processes
Illustration of the problem:Suppose that we wanted to provide a solution to the consumer-producer problem that fills all the buffers. We can do so by having an integer counter that keeps track of the number of full buffers. Initially, counter is set to 0. It is incremented by the producer after it produces a new buffer and is decremented by the consumer after it consumes a buffer
Operating system 24 mutex locks and semaphoresVaibhav Khanna
Mutual Exclusion - If process Pi is executing in its critical section, then no other processes can be executing in their critical sections
2. Progress - If no process is executing in its critical section and there exist some processes that wish to enter their critical section, then the selection of the processes that will enter the critical section next cannot be postponed indefinitely
3. Bounded Waiting - A bound must exist on the number of times that other processes are allowed to enter their critical sections after a process has made a request to enter its critical section and before that request is granted
Assume that each process executes at a nonzero speed
No assumption concerning relative speed of the n processes
This presentation contains an overview of novelties in ARMv8-A and details on application binary interface (ABI), memory management unit (MMU), caches and interrupts.
This talk was held within GlobalLogic Lviv Embedded TechTalk on November 23d, 2017.
Problem Decomposition: Goal Trees, Rule Based Systems, Rule Based Expert Systems. Planning:
STRIPS, Forward and Backward State Space Planning, Goal Stack Planning, Plan Space Planning,
A Unified Framework For Planning. Constraint Satisfaction : N-Queens, Constraint Propagation,
Scene Labeling, Higher order and Directional Consistencies, Backtracking and Look ahead
Strategies.
Operating system - Process and its conceptsKaran Thakkar
This presentation gives an overview of Process concepts in Operating System. The presentation aims at alleviating most of the overheads while understanding the process concept in operating system. this tailor made presentation will help individuals to understand the overall meaning of process and its underlying concepts used in an operating system.
Maximum CPU utilization obtained with multiprogramming
CPU–I/O Burst Cycle – Process execution consists of a cycle of CPU execution and I/O wait
CPU burst followed by I/O burst
CPU burst distribution is of main concern
Operating system 23 process synchronizationVaibhav Khanna
Processes can execute concurrently
May be interrupted at any time, partially completing execution
Concurrent access to shared data may result in data inconsistency
Maintaining data consistency requires mechanisms to ensure the orderly execution of cooperating processes
Illustration of the problem:Suppose that we wanted to provide a solution to the consumer-producer problem that fills all the buffers. We can do so by having an integer counter that keeps track of the number of full buffers. Initially, counter is set to 0. It is incremented by the producer after it produces a new buffer and is decremented by the consumer after it consumes a buffer
Operating system 24 mutex locks and semaphoresVaibhav Khanna
Mutual Exclusion - If process Pi is executing in its critical section, then no other processes can be executing in their critical sections
2. Progress - If no process is executing in its critical section and there exist some processes that wish to enter their critical section, then the selection of the processes that will enter the critical section next cannot be postponed indefinitely
3. Bounded Waiting - A bound must exist on the number of times that other processes are allowed to enter their critical sections after a process has made a request to enter its critical section and before that request is granted
Assume that each process executes at a nonzero speed
No assumption concerning relative speed of the n processes
This presentation contains an overview of novelties in ARMv8-A and details on application binary interface (ABI), memory management unit (MMU), caches and interrupts.
This talk was held within GlobalLogic Lviv Embedded TechTalk on November 23d, 2017.
Problem Decomposition: Goal Trees, Rule Based Systems, Rule Based Expert Systems. Planning:
STRIPS, Forward and Backward State Space Planning, Goal Stack Planning, Plan Space Planning,
A Unified Framework For Planning. Constraint Satisfaction : N-Queens, Constraint Propagation,
Scene Labeling, Higher order and Directional Consistencies, Backtracking and Look ahead
Strategies.
we will discuss important topics related to multi-master setups:
* Practical considerations when using Galera in a multi-master setup
* Evaluating the characteristics of your database workload
* Preparing your application for multi-master
* Detecting and dealing with transaction conflicts
Scalar DB: A library that makes non-ACID databases ACID-compliantScalar, Inc.
Scalar DB is a library that makes non-ACID databases ACID-compliant. It not only supports strongly-consistent ACID transactions, but also scales linearly and achieves high availability when it is deployed with distributed databases such as Cassandra.
Ceph at Work in Bloomberg: Object Store, RBD and OpenStackRed_Hat_Storage
Bloomberg's Chris Jones and Chris Morgan joined Red Hat Storage Day New York on 1/19/16 to explain how Red Hat Ceph Storage helps the financial giant tackle its data storage challenges.
Retaining Goodput with Query Rate LimitingScyllaDB
Distributed systems are usually optimized with particular workloads in mind. At the same time, the system should still behave in a sane way when the assumptions about workload do not hold - notably, one user shouldn't be able to ruin the whole system's performance. Buggy parts of the system can be a source of the overload as well, so it is worth considering overload protection on a per-component basis. For example, ScyllaDB's shared-nothing architecture gives it great scalability, but at the same time makes it prone to a "hot partition" problem: a single partition accessed with disproportionate frequency can ruin performance for other requests handled by the same shards. This talk will describe how we implemented rate limiting on a per-partition basis which reduces the performance impact in such a case, and how we reduced the CPU cost of handling failed requests such as timeouts (spoiler: it's about C++ exceptions).
Retaining Goodput with Query Rate LimitingScyllaDB
Distributed systems are usually optimized with particular workloads in mind. At the same time, the system should still behave in a sane way when the assumptions about workload do not hold - notably, one user shouldn't be able to ruin the whole system's performance. Buggy parts of the system can be a source of the overload as well, so it is worth considering overload protection on a per-component basis. For example, ScyllaDB's shared-nothing architecture gives it great scalability, but at the same time makes it prone to a "hot partition" problem: a single partition accessed with disproportionate frequency can ruin performance for other requests handled by the same shards. This talk will describe how we implemented rate limiting on a per-partition basis which reduces the performance impact in such a case, and how we reduced the CPU cost of handling failed requests such as timeouts (spoiler: it's about C++ exceptions).
If you're building relational, time-series, IOT, or real-time architectures using Hadoop, you will find Apache Kudu an attractive choice. With Kudu, you'll be able to build your applications more simply and with fewer moving parts.
Hadoop has become faster and more capable, and has continued to narrow the gap compared to traditional database technologies. However, for developers looking for up-to-the-second analytics on fast-moving data, some important gaps remain that prevent many applications from transitioning to Hadoop-based architectures. Users are often caught between a rock and a hard place: columnar formats such as Apache Parquet offer extremely fast scan rates for analytics, but little to no ability for real-time modification or row-by-row indexed access. Online systems such as HBase offer very fast random access, but scan rates that are too slow for large scale data warehousing and analytical workloads.
This talk will describe Kudu, the new addition to the open source Hadoop ecosystem with out-of-the-box integration with Apache Spark and Apache Impala. Kudu fills the gap described above to provide a new option to achieve fast scans and fast random access from a single API.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
5. Load / Store Processing
●
For both Loads and Stores:
●
Effective Address Generation:
–
–
●
Must wait on register value
Must perform address calculation
Address Translation:
–
●
Must access TLB, Can potentially induce a page fault (exception)
For Loads: D-cache Access (Read)
–
–
Check aliasing against store buffer for possible load forwarding
–
●
Can potentially induce a D-cache miss
If bypassing store, must be flagged as “speculative” load until completion
For Stores: D-cache Access (Write)
–
When completing must check aliasing against “speculative” loads
–
After completion, wait in store buffer for access to D-cache
–
Can potentially induce a D-cache miss
5
6. LSU pipeline
●
RegFile Access
–
●
Address Generation
–
●
Read the source registers
Add base, displacement, immediate fields to generate an EA
Cache Access
–
–
Bank access if cache is multi-banked
–
●
Index into set, tag comparison for ways
TLB access
Results
–
–
●
Target registers write back for loads
Store buffer/cache updates for stores
Finish
–
Post instruction status (complete or flush etc)
6
7. Addressing modes
●
An addressing mode is a mechanism for specifying an address.
●
absolute: the address is provided directly
●
●
●
register: the address is provided indirectly, but specifying where (what register)
the address can be found.
displacement: the address is computed by adding a displacement to the
contents of a register
indexed: the address is computed by adding a displacement to the contents of
a register, and then adding in the contents of another register times some
constant.
7
9. Pipeline Arbitration
●
Loads/Stores from Issue Unit
●
Re-executing loads/stores that missed DL1 or DTLB
●
Line Fills from L2
●
Snoops from different agent in case of MP
●
Data Prefetches
9
10. Sub Units
●
Load/Store Engine
–
–
●
Load/Store execution pipeline
2-3 pipelines present in modern designs
L1 Data cache
–
Multi-banked for simultaneous access to same line from multiple pipelines
–
Bank conflicts between loads/stores and snoops
–
virtually/physically indexed
●
–
Virtual indexing helps simultaneous access to TLB, but needs handling
aliases.
WB/WT
●
WB saves bandwidth on writes to L2, but needs handling snoops
–
Inclusive/Exclusive
–
Line Size
10
11. Sub Units
●
Data TLBs
–
–
●
Caches Virtual to Physical translations
TLB miss will cause load or store to stall.
Load Miss Queue
–
Tracks line fill requests to L2
–
Ld/St that miss DL1 including ownership upgrades
–
Handles multiple ld/store misses to same cacheline
–
Restarts loads/stores as line fills arrive
●
Critical data forwarding to re-executing loads
●
L2Hit Restart for best load to use latency during L2 hit cases
●
Store Buffers
●
Load/Store Re-order queue
●
Data Prefetch
●
Exceptions
11
12. Alignments
●
Aligned
–
●
Aligned on an operand sized boundary
Unaligned
–
–
●
Access crossing operand sized boundary
Might get broken down into multiple access
Line Crossing
–
–
Broken down into 2 access and data gets merged together
–
●
Access crossing cachelines.
Not guaranteed to be atomic (both x86, Power)
Page Crossing
–
Access crossing page boundaries
–
Broken down into 2 access, 2 TLB/Page miss handling
12
16. Memory Data Dependencies
●
Memory Dependency Detection:
–
–
Effective addresses can depend on run-time data and other instructions
–
●
Must compute effective addresses of both memory references
Comparison of addresses require much wider comparators
Hard to handle memory dependencies
–
Memory address are much wider than register names (64bit vs 5bits)
–
Memory dependencies are not static
●
A load (or store) instruction’s address can change (e.g. loop)
–
Addresses need to be calculated and translated first
–
Memory instructions take longer to execute relative to other instructions
●
Cache misses can take 100s of cycles
●
TLB misses can take 100s of cycles
16
17. Simple In-order Load/store Processing:
Total Load-Store Order
●
●
●
Keep all loads and stores totally in order
However Loads and stores can execute out of order with respect to other types
of instructions while obeying register data-dependence
Question: So when can a store actually write to cache ?
–
What if we write to cache as it execute ?
17
18. Store Buffers
●
Stores
–
Allocate store buffer entry at DISPATCH (in-order)
–
When register value available, issue & calculate address (“finished”)
–
When all previous instructions retire, store considered completed
●
–
●
Store buffer split into “finished” and “completed” part though pointers
Completed stores go to memory/cache inorder
Loads
–
Loads remember the store buffer entry of the last store before them
–
A load can issues when address register value availabe and
●
All older stores are considered “completed”
●
Q1: What happens to Store buffer when say a branch mispredicts ?
●
Q2: What happens when a snoop hit a Store Buffer entry ?
18
20. Load Bypassing & Forwarding
●
Bypassing
–
–
Store addresses still need to be computed before loads can be issued to
allow checking for load dependences.
–
●
Loads can be allowed to bypass stores (if no aliasing).
If dependence cannot be checked, e.g. store address cannot be determined,
then all subsequent loads are held until address is valid (conservative).
Forwarding
–
If a subsequent load has a dependence on a store still in the store buffer, it
need not wait till the store is issued to the data cache.
–
The load can be directly satisfied from the store buffer if the address is
valid and the data is available in the store buffer.
20
21. Load Forwarding
Q: In case of multiple match, which store do we forward from ?
Q: In case of partial match, can we forward ?
21
22. Non-Speculative Disambiguation
●
Non-speculative load/store disambiguation
–
–
Full address comparison
–
●
Loads wait for addresses of all prior stores
Bypass if no match, forward if match
Can limit performance:
–
load r5,MEM[r3]
cache miss
–
store r7, MEM[r5]
RAW for agen, stalled
–
…
–
load r8, MEM[r9]
independent load stalled
22
23. Speculative Disambiguation
•
What if aliases are rare?
1.
2.
3.
4.
Loads don’t wait for addresses of all
prior stores
Full address comparison of stores that
are ready
Bypass if no match, forward if match
Check all store addresses when they
commit
–
–
5.
No matching loads – speculation was
correct
Matching unbypassed load – incorrect
speculation
Replay starting from incorrect load
27. Memory Dependence Prediction
●
If aliases are rare: static prediction
–
–
●
Predict no alias every time (Blind prediction)
Pay misprediction penalty rarely
If aliases are more frequent: dynamic prediction
–
Use some form of history tables for loads
–
Store Set Algorithm
●
●
●
Allow speculation of loads around stores when program starts
If a load and store causes violation, add the PC of store to the
load's store set.
Next time the load executes, it waits for all stores in the store
set
27
28. Prediction Implementation (Intel Core 2)
•
•
•
•
•
History table indexed by Instruction Pointer
Each entry in the history array has a saturating counter
Once counter saturates: disambiguation possible on this load (take
effect since next iteration) -load is allowed to go even meet unkown
store addresses
When a particular load failed disambiguation: reset its counter
Each time a particular load correctly disambiguated: increment
counter
29. Data Prefetching
●
S/W Prefetching
–
–
●
Instructions like prefetch (x86),
Cache touch instructions (Power)
H/W Prefetching
–
Speculation about future memory access patterns based on previous
patterns
–
Hardware monitors the processor's address reference pattern and issues
prefetch if a predictable memory address pattern is detected
29