Cache memory is a small, fast memory located between the CPU and main memory that temporarily stores frequently accessed data. It improves performance by providing faster access for the CPU compared to accessing main memory. There are different types of cache memory organization including direct mapping, set associative mapping, and fully associative mapping. Direct mapping maps each block of main memory to only one location in cache while set associative mapping divides the cache into sets with multiple lines per set allowing a block to map to any line within a set.
This document discusses cache memory and virtual memory. It begins by explaining the memory hierarchy from fastest to slowest of registers, cache, main memory, and magnetic disk. It describes how cache exploits locality through temporal and spatial locality. Different cache mapping techniques like direct mapping, set associative mapping, and fully associative mapping are covered. The document also discusses virtual memory and how it allows programs to access more memory than physically available using the memory management unit to translate virtual to physical addresses through page tables and the translation lookaside buffer.
SF Java presentation of jvm goes to big data.
“Slowly yet surely the JVM is going to Big Data! In this fun filled presentation we see what pieces of Java & JVM triumph or unravel in the battle for performance at high scale!”
Concurrency is the currency of scale on multi-core & the new generation of collections and non-blocking hashmaps are well worth the time taking a deep dive into. We take a quick look at the next gen serialization techniques as well as implementation pitfalls around UUID. The achilles' heel for JVM remains Garbage Collection: a deep dive into the internals of the memory model, common GC algorithms and their tuning knobs is always a big draw. EC2 & cloud present us with a virtualized & unchartered territory for scaling the JVM.
We will leave some room for Q&A or fill it up with any asynchronous I/O that might queue up during the talk. A round of applause will be due to the various tools that are essentials for Java performance debugging.
Java heap memory model has wasteful memory usage. References, object headers, internal collection structure, extra fields such as String.hashCode… This talk shows practical ways to reduce memory usage and fit more data into memory: primitive types, specialized java collections, bit packing, reducing number of pointers, replacing String with char[], semi-serialized objects… As bonus we get lower GC overhead by reducing number of references.
The document discusses cache design and organization. It describes how caches work, sitting between the CPU and main memory to provide fast access to frequently used data. The key aspects covered include cache size, block size, mapping techniques, replacement algorithms, write policies, and the evolution of cache hierarchies in processors like the Pentium IV with multiple levels of on-chip and off-chip caches.
This document summarizes key characteristics of cache memory including location, capacity, access methods, performance, and organization. It discusses the memory hierarchy from registers to external memory. Common cache mapping techniques like direct mapping, associative mapping, and set associative mapping are explained. The document also covers cache design considerations such as replacement algorithms and write policies.
This document discusses the key characteristics of computer memory, including location, capacity, unit of transfer, access methods, performance, physical type, physical characteristics, and organization. It covers different types of memory like CPU registers, main memory, cache, disk, and tape. The different access methods like sequential, direct, random, and associative access are explained. The memory hierarchy and performance aspects like access time, memory cycle time, and transfer rate are defined. Factors like cache size, mapping function, replacement algorithm, write policy, block size that impact cache performance are also summarized.
Cache memory is a small, fast memory located between the CPU and main memory that temporarily stores frequently accessed data. It improves performance by providing faster access for the CPU compared to accessing main memory. There are different types of cache memory organization including direct mapping, set associative mapping, and fully associative mapping. Direct mapping maps each block of main memory to only one location in cache while set associative mapping divides the cache into sets with multiple lines per set allowing a block to map to any line within a set.
This document discusses cache memory and virtual memory. It begins by explaining the memory hierarchy from fastest to slowest of registers, cache, main memory, and magnetic disk. It describes how cache exploits locality through temporal and spatial locality. Different cache mapping techniques like direct mapping, set associative mapping, and fully associative mapping are covered. The document also discusses virtual memory and how it allows programs to access more memory than physically available using the memory management unit to translate virtual to physical addresses through page tables and the translation lookaside buffer.
SF Java presentation of jvm goes to big data.
“Slowly yet surely the JVM is going to Big Data! In this fun filled presentation we see what pieces of Java & JVM triumph or unravel in the battle for performance at high scale!”
Concurrency is the currency of scale on multi-core & the new generation of collections and non-blocking hashmaps are well worth the time taking a deep dive into. We take a quick look at the next gen serialization techniques as well as implementation pitfalls around UUID. The achilles' heel for JVM remains Garbage Collection: a deep dive into the internals of the memory model, common GC algorithms and their tuning knobs is always a big draw. EC2 & cloud present us with a virtualized & unchartered territory for scaling the JVM.
We will leave some room for Q&A or fill it up with any asynchronous I/O that might queue up during the talk. A round of applause will be due to the various tools that are essentials for Java performance debugging.
Java heap memory model has wasteful memory usage. References, object headers, internal collection structure, extra fields such as String.hashCode… This talk shows practical ways to reduce memory usage and fit more data into memory: primitive types, specialized java collections, bit packing, reducing number of pointers, replacing String with char[], semi-serialized objects… As bonus we get lower GC overhead by reducing number of references.
The document discusses cache design and organization. It describes how caches work, sitting between the CPU and main memory to provide fast access to frequently used data. The key aspects covered include cache size, block size, mapping techniques, replacement algorithms, write policies, and the evolution of cache hierarchies in processors like the Pentium IV with multiple levels of on-chip and off-chip caches.
This document summarizes key characteristics of cache memory including location, capacity, access methods, performance, and organization. It discusses the memory hierarchy from registers to external memory. Common cache mapping techniques like direct mapping, associative mapping, and set associative mapping are explained. The document also covers cache design considerations such as replacement algorithms and write policies.
This document discusses the key characteristics of computer memory, including location, capacity, unit of transfer, access methods, performance, physical type, physical characteristics, and organization. It covers different types of memory like CPU registers, main memory, cache, disk, and tape. The different access methods like sequential, direct, random, and associative access are explained. The memory hierarchy and performance aspects like access time, memory cycle time, and transfer rate are defined. Factors like cache size, mapping function, replacement algorithm, write policy, block size that impact cache performance are also summarized.
Cache memory is used to improve processor performance by making main memory access appear faster. It works based on the principle of locality of reference, where programs tend to access the same data/instructions repeatedly. A cache hit provides faster access than main memory, while a miss requires retrieving data from main memory. Caches use mapping functions like direct, associative, or set-associative mapping to determine where to place blocks of data from main memory.
The document describes the mechanism of msgpack::unpacker, which is a library for unpacking MessagePack serialized data in C++. It discusses two scenarios:
1) Receiving msgpack data in fixed length packets with a length header. The unpacker allocates a buffer based on the length, unpacks the data, and checks if more data is needed on each call.
2) Receiving a continuous msgpack stream without length headers. The unpacker allocates an initial buffer and resizes it as needed, unpacking data in chunks to avoid copying. It handles partially unpacked objects across buffer resizes.
The internal structures of the unpacker are also outlined, including how it manages memory buffers
There are three main methods to map main memory addresses to cache memory addresses: direct mapping, associative mapping, and set-associative mapping. Direct mapping is the simplest but least flexible method, while associative mapping is most flexible but also slowest. Set-associative mapping combines aspects of the other two methods, dividing the cache into sets with multiple lines to gain efficiency while remaining reasonably flexible.
Memory mapping techniques and low power memory designUET Taxila
This document discusses memory mapping techniques and low power memory design. It describes three main memory mapping techniques: direct mapping, fully-associative mapping, and set-associative mapping. It then discusses a proposed method for low power off-chip memory design for video decoders using an embedded bus-invert coding scheme. The method aims to minimize power consumption of external memory in an efficient way without increasing algorithm complexity or requiring system modifications.
Explain cache memory with a diagram, demonstrate hit ratio and miss penalty with an example. Discussed different types of cache mapping: direct mapping, fully-associative mapping and set-associative mapping. Discussed temporal and spatial locality of references in cache memory. Explained cache write policies: write through and write back. Shown the differences between unified cache and split cache.
Cache memory is a small, high-speed memory located between the CPU and main memory. It stores copies of frequently used instructions and data from main memory in order to speed up processing. There are multiple levels of cache with L1 cache being the smallest and fastest located directly on the CPU chip. Larger cache levels like L2 and L3 are further from the CPU but can still provide faster access than main memory. The main purpose of cache is to accelerate processing speed while keeping computer costs low.
This document discusses various aspects of computer memory systems including cache memory. It begins by defining key terms related to memory such as capacity, organization, access methods, and physical characteristics. It then covers cache memory in particular, explaining the basic concept of caching as well as aspects of cache design like mapping, replacement algorithms, and write policies. Examples of cache configurations from different processor models over time are also provided.
The Presentation introduces the basic concept of cache memory, its introduction , background and all necessary details are provided along with details of different mapping techniques that are used inside Cache Memory.
This document discusses cache memory and its characteristics. It begins by defining cache memory as a smaller, faster memory located close to the CPU that stores copies of frequently accessed data from main memory. This is done to achieve higher CPU performance by allowing faster access to cached data compared to main memory. The document then covers various characteristics of cache memory like location, capacity, unit of transfer, access methods, performance, organization, mapping functions, replacement algorithms, and write policies. Diagrams are included to illustrate cache read operations and different mapping approaches.
This document summarizes key characteristics of cache memory including location, capacity, unit of transfer, access methods, performance, physical types, organization, and memory hierarchy. It discusses different cache mapping techniques like direct mapping, set associative mapping, and fully associative mapping. The document also covers cache performance factors like hit ratio, replacement algorithms, write policies, line size, and multilevel caches. As an example, it analyzes the cache architecture of Intel Pentium 4 processor.
There are three main methods for mapping memory addresses to cache addresses: direct mapping, associative mapping, and set-associative mapping. Direct mapping maps each block of main memory to a single block in cache in a one-to-one manner. Associative mapping allows any block of main memory to be mapped to any block in cache but requires tag bits to identify blocks. Set-associative mapping groups cache blocks into sets, with a main memory block mapped to a particular set and then flexibly to a block within that set, providing more flexibility than direct mapping but less complexity than full associative mapping.
The document discusses cache organization and mapping techniques. It describes:
1) Direct mapping where each block maps to one line. Set associative mapping divides cache into sets with multiple lines per set.
2) Replacement algorithms like FIFO and LRU that determine which block to replace when the cache is full.
3) Write policies like write-through and write-back that handle writing cached data back to main memory.
About Cache Memory
working of cache memory
levels of cache memory
mapping techniques for cache memory
1. direct mapping techniques
2. Fully associative mapping techniques
3. set associative mapping techniques
Cache memroy organization
cache coherency
every thing in detail
Cache memory is a small, fast memory located close to the CPU that stores frequently accessed instructions and data. It aims to bridge the gap between the fast CPU and slower main memory. Cache memory is organized into blocks that each contain a tag field identifying the memory address, a data field containing the cached data, and status bits. There are different mapping techniques like direct mapping, associative mapping, and set associative mapping to determine how blocks are stored in cache. When cache is full, replacement algorithms like LRU, FIFO, LFU, and random are used to determine which existing block to replace with the new block.
Cache memory is a type of memory that stores frequently accessed data closer to the processor for low latency access. There are different types of caches including local, replicated, distributed, remote, and near caches.
Direct mapping is a technique that maps each block of main memory to only one cache line. For a 64KB cache with 4 byte blocks and 16MB of main memory addressed with 24 bits, the address is split into a 2-bit word identifier, 8-bit tag, and 14-bit slot.
There are two policies for writing to cache - write-through always writes to both cache and main memory, while write-back only writes to cache and defers writing to main memory until the block is replaced
Cache memory is a small, fast memory located close to the processor that stores frequently accessed data from main memory. When the processor requests data, the cache is checked first. If the data is present, there is a cache hit and the data is accessed quickly from the cache. If not present, there is a cache hit and the data must be fetched from main memory, which takes longer. Cache memory relies on principles of temporal and spatial locality, where frequently and nearby accessed data is likely to be needed again soon. Mapping functions like direct, associative, and set-associative mapping determine how data is stored in the cache. Replacement policies like FIFO, LRU, etc. determine which cached data gets replaced when new
This document discusses cache memory organization and characteristics. It begins by describing cache location, capacity, unit of transfer, access methods, and physical characteristics. It then covers the different mapping techniques used in caches, including direct mapping, set associative mapping, and fully associative mapping. The document also discusses cache performance factors like hit ratio, replacement algorithms, write policies, block size, and multilevel cache hierarchies. It provides examples of specific processor cache designs like those used in Intel Pentium processors.
This document discusses cache memory principles and provides details about cache operation, structure, organization, and design considerations. The key points covered are:
- Cache is a small, fast memory located between the CPU and main memory that stores frequently used data.
- During a cache read operation, the CPU first checks the cache for the requested data. If present, it is retrieved from the fast cache. If not, the data is read from main memory into cache.
- Cache design considerations include size, mapping function, replacement algorithm, write policy, line size, and number of cache levels.
- Modern CPUs use hierarchical cache designs with multiple levels (L1, L2, etc.) to improve performance.
20 Ideas for your Website Homepage ContentBarry Feldman
Perplexed about what to put on your website home? Every company deals with this tough challenge. The 20 ideas in this presentation should give you a strong starting point.
This document outlines 50 essential content marketing hacks presented by Matt Heinz, President of Heinz Marketing Inc. at CMWorld. It provides an agenda for the presentation and covers topics such as content planning, measurement, formats, distribution, influencer engagement, repurposing content, and getting sales teams to leverage content. The goal is to provide new tools, tricks and best practices to help convert readers into customers through effective content marketing.
Cache memory is used to improve processor performance by making main memory access appear faster. It works based on the principle of locality of reference, where programs tend to access the same data/instructions repeatedly. A cache hit provides faster access than main memory, while a miss requires retrieving data from main memory. Caches use mapping functions like direct, associative, or set-associative mapping to determine where to place blocks of data from main memory.
The document describes the mechanism of msgpack::unpacker, which is a library for unpacking MessagePack serialized data in C++. It discusses two scenarios:
1) Receiving msgpack data in fixed length packets with a length header. The unpacker allocates a buffer based on the length, unpacks the data, and checks if more data is needed on each call.
2) Receiving a continuous msgpack stream without length headers. The unpacker allocates an initial buffer and resizes it as needed, unpacking data in chunks to avoid copying. It handles partially unpacked objects across buffer resizes.
The internal structures of the unpacker are also outlined, including how it manages memory buffers
There are three main methods to map main memory addresses to cache memory addresses: direct mapping, associative mapping, and set-associative mapping. Direct mapping is the simplest but least flexible method, while associative mapping is most flexible but also slowest. Set-associative mapping combines aspects of the other two methods, dividing the cache into sets with multiple lines to gain efficiency while remaining reasonably flexible.
Memory mapping techniques and low power memory designUET Taxila
This document discusses memory mapping techniques and low power memory design. It describes three main memory mapping techniques: direct mapping, fully-associative mapping, and set-associative mapping. It then discusses a proposed method for low power off-chip memory design for video decoders using an embedded bus-invert coding scheme. The method aims to minimize power consumption of external memory in an efficient way without increasing algorithm complexity or requiring system modifications.
Explain cache memory with a diagram, demonstrate hit ratio and miss penalty with an example. Discussed different types of cache mapping: direct mapping, fully-associative mapping and set-associative mapping. Discussed temporal and spatial locality of references in cache memory. Explained cache write policies: write through and write back. Shown the differences between unified cache and split cache.
Cache memory is a small, high-speed memory located between the CPU and main memory. It stores copies of frequently used instructions and data from main memory in order to speed up processing. There are multiple levels of cache with L1 cache being the smallest and fastest located directly on the CPU chip. Larger cache levels like L2 and L3 are further from the CPU but can still provide faster access than main memory. The main purpose of cache is to accelerate processing speed while keeping computer costs low.
This document discusses various aspects of computer memory systems including cache memory. It begins by defining key terms related to memory such as capacity, organization, access methods, and physical characteristics. It then covers cache memory in particular, explaining the basic concept of caching as well as aspects of cache design like mapping, replacement algorithms, and write policies. Examples of cache configurations from different processor models over time are also provided.
The Presentation introduces the basic concept of cache memory, its introduction , background and all necessary details are provided along with details of different mapping techniques that are used inside Cache Memory.
This document discusses cache memory and its characteristics. It begins by defining cache memory as a smaller, faster memory located close to the CPU that stores copies of frequently accessed data from main memory. This is done to achieve higher CPU performance by allowing faster access to cached data compared to main memory. The document then covers various characteristics of cache memory like location, capacity, unit of transfer, access methods, performance, organization, mapping functions, replacement algorithms, and write policies. Diagrams are included to illustrate cache read operations and different mapping approaches.
This document summarizes key characteristics of cache memory including location, capacity, unit of transfer, access methods, performance, physical types, organization, and memory hierarchy. It discusses different cache mapping techniques like direct mapping, set associative mapping, and fully associative mapping. The document also covers cache performance factors like hit ratio, replacement algorithms, write policies, line size, and multilevel caches. As an example, it analyzes the cache architecture of Intel Pentium 4 processor.
There are three main methods for mapping memory addresses to cache addresses: direct mapping, associative mapping, and set-associative mapping. Direct mapping maps each block of main memory to a single block in cache in a one-to-one manner. Associative mapping allows any block of main memory to be mapped to any block in cache but requires tag bits to identify blocks. Set-associative mapping groups cache blocks into sets, with a main memory block mapped to a particular set and then flexibly to a block within that set, providing more flexibility than direct mapping but less complexity than full associative mapping.
The document discusses cache organization and mapping techniques. It describes:
1) Direct mapping where each block maps to one line. Set associative mapping divides cache into sets with multiple lines per set.
2) Replacement algorithms like FIFO and LRU that determine which block to replace when the cache is full.
3) Write policies like write-through and write-back that handle writing cached data back to main memory.
About Cache Memory
working of cache memory
levels of cache memory
mapping techniques for cache memory
1. direct mapping techniques
2. Fully associative mapping techniques
3. set associative mapping techniques
Cache memroy organization
cache coherency
every thing in detail
Cache memory is a small, fast memory located close to the CPU that stores frequently accessed instructions and data. It aims to bridge the gap between the fast CPU and slower main memory. Cache memory is organized into blocks that each contain a tag field identifying the memory address, a data field containing the cached data, and status bits. There are different mapping techniques like direct mapping, associative mapping, and set associative mapping to determine how blocks are stored in cache. When cache is full, replacement algorithms like LRU, FIFO, LFU, and random are used to determine which existing block to replace with the new block.
Cache memory is a type of memory that stores frequently accessed data closer to the processor for low latency access. There are different types of caches including local, replicated, distributed, remote, and near caches.
Direct mapping is a technique that maps each block of main memory to only one cache line. For a 64KB cache with 4 byte blocks and 16MB of main memory addressed with 24 bits, the address is split into a 2-bit word identifier, 8-bit tag, and 14-bit slot.
There are two policies for writing to cache - write-through always writes to both cache and main memory, while write-back only writes to cache and defers writing to main memory until the block is replaced
Cache memory is a small, fast memory located close to the processor that stores frequently accessed data from main memory. When the processor requests data, the cache is checked first. If the data is present, there is a cache hit and the data is accessed quickly from the cache. If not present, there is a cache hit and the data must be fetched from main memory, which takes longer. Cache memory relies on principles of temporal and spatial locality, where frequently and nearby accessed data is likely to be needed again soon. Mapping functions like direct, associative, and set-associative mapping determine how data is stored in the cache. Replacement policies like FIFO, LRU, etc. determine which cached data gets replaced when new
This document discusses cache memory organization and characteristics. It begins by describing cache location, capacity, unit of transfer, access methods, and physical characteristics. It then covers the different mapping techniques used in caches, including direct mapping, set associative mapping, and fully associative mapping. The document also discusses cache performance factors like hit ratio, replacement algorithms, write policies, block size, and multilevel cache hierarchies. It provides examples of specific processor cache designs like those used in Intel Pentium processors.
This document discusses cache memory principles and provides details about cache operation, structure, organization, and design considerations. The key points covered are:
- Cache is a small, fast memory located between the CPU and main memory that stores frequently used data.
- During a cache read operation, the CPU first checks the cache for the requested data. If present, it is retrieved from the fast cache. If not, the data is read from main memory into cache.
- Cache design considerations include size, mapping function, replacement algorithm, write policy, line size, and number of cache levels.
- Modern CPUs use hierarchical cache designs with multiple levels (L1, L2, etc.) to improve performance.
20 Ideas for your Website Homepage ContentBarry Feldman
Perplexed about what to put on your website home? Every company deals with this tough challenge. The 20 ideas in this presentation should give you a strong starting point.
This document outlines 50 essential content marketing hacks presented by Matt Heinz, President of Heinz Marketing Inc. at CMWorld. It provides an agenda for the presentation and covers topics such as content planning, measurement, formats, distribution, influencer engagement, repurposing content, and getting sales teams to leverage content. The goal is to provide new tools, tricks and best practices to help convert readers into customers through effective content marketing.
The document discusses prototyping and provides examples of different types of prototypes including paper prototypes, digital prototypes, storyboards, role plays, and space prototypes. It explains that prototyping is used to make ideas tangible and test reactions from users in order to gain insights. Prototypes should be iterated on and fail early to push ideas further and save time and money. Both low and high fidelity prototypes are mentioned as ways to test ideas at different stages of the design process.
10 Insightful Quotes On Designing A Better Customer ExperienceYuan Wang
In an ever-changing landscape of one digital disruption after another, companies and organisations are looking for new ways to understand their target markets and engage them better. Increasingly they invest in user experience (UX) and customer experience design (CX) capabilities by working with a specialist UX agency or developing their own UX lab. Some UX practitioners are touting leaner and faster ways of developing customer-centric products and services, via methodologies such as guerilla research, rapid prototyping and Agile UX. Others seek innovation and fulfilment by spending more time in research, being more inclusive, and designing for social goods.
Experience is more than just an interface. It is a relationship, as well as a series of touch points between your brand and your customer. Here are our top 10 highlights and takeaways from the recent UX Australia conference to help you transform your customer experience design.
For full article, continue reading at https://yump.com.au/10-ways-supercharge-customer-experience-design/
How to Build a Dynamic Social Media PlanPost Planner
Stop guessing and wasting your time on networks and strategies that don’t work!
Join Rebekah Radice and Katie Lance to learn how to optimize your social networks, the best kept secrets for hot content, top time management tools, and much more!
Watch the replay here: bit.ly/socialmedia-plan
http://inarocket.com
Learn BEM fundamentals as fast as possible. What is BEM (Block, element, modifier), BEM syntax, how it works with a real example, etc.
The document discusses how personalization and dynamic content are becoming increasingly important on websites. It notes that 52% of marketers see content personalization as critical and 75% of consumers like it when brands personalize their content. However, personalization can create issues for search engine optimization as dynamic URLs and content are more difficult for search engines to index than static pages. The document provides tips for SEOs to help address these personalization and SEO challenges, such as using static URLs when possible and submitting accurate sitemaps.
This document provides an overview of embedded C programming concepts including:
- The C preprocessor and directives like #define, #include, #if.
- Bitwise operations like bit masking, setting, clearing, and toggling bits.
- Type qualifiers like const and volatile and their usage.
- Compiler optimization levels and tradeoffs between execution time, code size, and memory usage.
- Enumerations and typedef for defining standard data types.
- Design concepts like layered architectures and finite state machines.
- The contents and purpose of object files like .text, .data, .bss sections.
- AUTOSAR architecture with layers like MCAL, ECUAL, and services layer.
The reasons why 64-bit programs require more stack memoryPVS-Studio
In forums, people often say that 64-bit versions of programs consume a larger amount of memory and stack. Saying so, they usually argue that the sizes of data have become twice larger. But this statement is unfounded since the size of most types (char, short, int, float) in the C/C++ language remains the same on 64-bit systems. Of course, for instance, the size of a pointer has increased but far not all the data in a program consist of pointers. The reasons why the memory amount consumed by programs has increased are more complex. I decided to investigate this issue in detail.
Advanced High-Performance Computing Features of the OpenPOWER ISAGanesan Narayanasamy
Power ISA processors have a long history of offering superior features for HPC applications. Well known examples include POWER3, used in the ASCI White supercomputer, various PowerPC processors used in the Blue Gene family of massively parallel computers, and POWER9, present in the leading supercomputers of today, Summit and Sierra. OpenPOWER ISA has enabled open access to many of these features. IBM's most recent contribution to OpenPOWER ISA, in the form of Power ISA Version 3.1, includes the Matrix-Multiply Assist (MMA) instructions. The MMA instructions are designed to deliver additional performance both for classical high-performance computing, in the space of scientific and technical computing, and for the increasingly important space of business analytics. In addition, the Open Memory Interface (OMI), also developed by IBM, opens new levels of memory bandwidth and capacity for the most demanding applications. Our goal is to raise awareness of and interest in these new features, which we believe can lead to further research in processor architecture and programming environments. Some of the most promising application areas include graph algorithms, classical machine learning and deep learning.
Comparison of analyzers' diagnostic possibilities at checking 64-bit codePVS-Studio
The article compares a specialized static analyzer Viva64 with universal static analyzers Parasoft C++Test and Gimpel Software PC-Lint. The comparison is carried within the framework of the task of porting 32-bit C/C++ code on 64-bit systems or developing new code with taking into account peculiarities of 64-bit architecture.
FOSDEM 2019: M3, Prometheus and Graphite with metrics and monitoring in an in...Rob Skillington
Rob Skillington gave a presentation on observability and M3, Uber's open source time series database. Some key points:
- M3 was created at Uber to handle high dimensionality metrics at massive scale, storing over 11 billion unique time series.
- It uses techniques like Roaring Bitmaps to efficiently store and query metrics with many dimensions or tag values.
- M3 can ingest metrics from Prometheus and Graphite, storing over 33 million metrics per second while powering dashboards and 150,000 alerts.
- The open source M3DB component can run standalone or on Kubernetes, providing a scalable time series storage solution for complex monitoring needs.
Please do ECE572 requirementECECS 472572 Final Exam Project (W.docxARIV4
Please do ECE572 requirement
ECE/CS 472/572 Final Exam Project (Whole question is attached in word file)
Remember to check the errata section (at the very bottom of the page) for updates.
Your submission should be comprised of two items:
a
.pdf
file containing your written report and a
.tar
file containing a directory structure with your C or C++ source code. Your grade will be reduced if you do not follow the submission instructions.
All written reports (for both 472 and 572 students) must be composed in MS Word, LaTeX, or some other word processor and submitted as a PDF file.
Please take the time to read this entire document. If you have questions there is a high likelihood that another section of the document provides answers.
Introduction
In this final project you will implement a cache simulator. Your simulator will be configurable and will be able to handle caches with varying capacities, block sizes, levels of associativity, replacement policies, and write policies. The simulator will operate on trace files that indicate memory access properties. All input files to your simulator will follow a specific structure so that you can parse the contents and use the information to set the properties of your simulator.
After execution is finished, your simulator will generate an output file containing information on the number of cache misses, hits, and miss evictions (i.e. the number of block replacements). In addition, the file will also record the total number of (simulated) clock cycles used during the situation. Lastly, the file will indicate how many read and write operations were requested by the CPU.
It is important to note that your simulator is required to make several significant assumptions for the sake of simplicity.
1. You do not have to simulate the actual data contents. We simply pretend that we copied data from main memory and keep track of the hypothetical time that would have elapsed.
2. Accessing a sub-portion of a cache block takes the exact same time as it would require to access the entire block. Imagine that you are working with a cache that uses a 32 byte block size and has an access time of 15 clock cycles. Reading a 32 byte block from this cache will require 15 clock cycles. However, the same amount of time is required to read 1 byte from the cache.
3. In this project assume that main memory RAM is always accessed in units of 8 bytes (i.e. 64 bits at a time).
When accessing main memory, it's expensive to access the first unit. However, DDR memory typically includes buffering which means that the RAM can provide access to the successive memory (in 8 byte chunks) with minimal overhead. In this project we assume an
overhead of 1 additional clock cycle per contiguous unit
.
For example, suppose that it costs 255 clock cycles to access the first unit from main memory. Based on our assumption, it would only cost 257 clock cycles to access 24 bytes of memory.
4. Assume that all caches utilize a "fetch-.
2021 04-20 apache arrow and its impact on the database industry.pptxAndrew Lamb
The talk will motivate why Apache Arrow and related projects (e.g. DataFusion) is a good choice for implementing modern analytic database systems. It reviews the major components in most databases and explains where Apache Arrow fits in, and explains additional integration benefits from using Arrow.
Next Generation Indexes For Big Data Engineering (ODSC East 2018)Daniel Lemire
Maximizing performance in data engineering is a daunting challenge. We present some of our work on designing faster indexes, with a particular emphasis on compressed indexes. Some of our prior work includes (1) Roaring indexes which are part of multiple big-data systems such as Spark, Hive, Druid, Atlas, Pinot, Kylin, (2) EWAH indexes are part of Git (GitHub) and included in major Linux distributions.
We will present ongoing and future work on how we can process data faster while supporting the diverse systems found in the cloud (with upcoming ARM processors) and under multiple programming languages (e.g., Java, C++, Go, Python). We seek to minimize shared resources (e.g., RAM) while exploiting algorithms designed for the single-instruction-multiple-data (SIMD) instructions available on commodity processors. Our end goal is to process billions of records per second per core.
The talk will be aimed at programmers who want to better understand the performance characteristics of current big-data systems as well as their evolution. The following specific topics will be addressed:
1. The various types of indexes and their performance characteristics and trade-offs: hashing, sorted arrays, bitsets and so forth.
2. Index and table compression techniques: binary packing, patched coding, dictionary coding, frame-of-reference.
ECECS 472572 Final Exam ProjectRemember to check the errata EvonCanales257
ECE/CS 472/572 Final Exam Project
Remember to check the errata section (at the very bottom of the page) for updates.
Your submission should be comprised of two items: a .pdf file containing your written report and a .tar file containing a directory structure with your C or C++ source code. Your grade will be reduced if you do not follow the submission instructions.
All written reports (for both 472 and 572 students) must be composed in MS Word, LaTeX, or some other word processor and submitted as a PDF file.
Please take the time to read this entire document. If you have questions there is a high likelihood that another section of the document provides answers.
Introduction
In this final project you will implement a cache simulator. Your simulator will be configurable and will be able to handle caches with varying capacities, block sizes, levels of associativity, replacement policies, and write policies. The simulator will operate on trace files that indicate memory access properties. All input files to your simulator will follow a specific structure so that you can parse the contents and use the information to set the properties of your simulator.
After execution is finished, your simulator will generate an output file containing information on the number of cache misses, hits, and miss evictions (i.e. the number of block replacements). In addition, the file will also record the total number of (simulated) clock cycles used during the situation. Lastly, the file will indicate how many read and write operations were requested by the CPU.
It is important to note that your simulator is required to make several significant assumptions for the sake of simplicity.
1. You do not have to simulate the actual data contents. We simply pretend that we copied data from main memory and keep track of the hypothetical time that would have elapsed.
2. Accessing a sub-portion of a cache block takes the exact same time as it would require to access the entire block. Imagine that you are working with a cache that uses a 32 byte block size and has an access time of 15 clock cycles. Reading a 32 byte block from this cache will require 15 clock cycles. However, the same amount of time is required to read 1 byte from the cache.
3. In this project assume that main memory RAM is always accessed in units of 8 bytes (i.e. 64 bits at a time).
When accessing main memory, it's expensive to access the first unit. However, DDR memory typically includes buffering which means that the RAM can provide access to the successive memory (in 8 byte chunks) with minimal overhead. In this project we assume an overhead of 1 additional clock cycle per contiguous unit.
For example, suppose that it costs 255 clock cycles to access the first unit from main memory. Based on our assumption, it would only cost 257 clock cycles to access 24 bytes of memory.
4. Assume that all caches utilize a "fetch-on-write" scheme if a miss occurs on a Store operation. This means that you must always fetch ...
ECECS 472572 Final Exam ProjectRemember to check the errat.docxtidwellveronique
ECE/CS 472/572 Final Exam Project
Remember to check the errata section (at the very bottom of the page) for updates.
Your submission should be comprised of two items:
a
.pdf
file containing your written report and a
.tar
file containing a directory structure with your C or C++ source code. Your grade will be reduced if you do not follow the submission instructions.
All written reports (for both 472 and 572 students) must be composed in MS Word, LaTeX, or some other word processor and submitted as a PDF file.
Please take the time to read this entire document. If you have questions there is a high likelihood that another section of the document provides answers.
Introduction
In this final project you will implement a cache simulator. Your simulator will be configurable and will be able to handle caches with varying capacities, block sizes, levels of associativity, replacement policies, and write policies. The simulator will operate on trace files that indicate memory access properties. All input files to your simulator will follow a specific structure so that you can parse the contents and use the information to set the properties of your simulator.
After execution is finished, your simulator will generate an output file containing information on the number of cache misses, hits, and miss evictions (i.e. the number of block replacements). In addition, the file will also record the total number of (simulated) clock cycles used during the situation. Lastly, the file will indicate how many read and write operations were requested by the CPU.
It is important to note that your simulator is required to make several significant assumptions for the sake of simplicity.
1. You do not have to simulate the actual data contents. We simply pretend that we copied data from main memory and keep track of the hypothetical time that would have elapsed.
2. Accessing a sub-portion of a cache block takes the exact same time as it would require to access the entire block. Imagine that you are working with a cache that uses a 32 byte block size and has an access time of 15 clock cycles. Reading a 32 byte block from this cache will require 15 clock cycles. However, the same amount of time is required to read 1 byte from the cache.
3. In this project assume that main memory RAM is always accessed in units of 8 bytes (i.e. 64 bits at a time).
When accessing main memory, it's expensive to access the first unit. However, DDR memory typically includes buffering which means that the RAM can provide access to the successive memory (in 8 byte chunks) with minimal overhead. In this project we assume an
overhead of 1 additional clock cycle per contiguous unit
.
For example, suppose that it costs 255 clock cycles to access the first unit from main memory. Based on our assumption, it would only cost 257 clock cycles to access 24 bytes of memory.
4. Assume that all caches utilize a "fetch-on-write" scheme if a miss occurs on a Store operation. This means that .
ECECS 472572 Final Exam ProjectRemember to check the err.docxtidwellveronique
ECE/CS 472/572 Final Exam Project
Remember to check the errata section (at the very bottom of the page) for updates.
Your submission should be comprised of two items:
a
.pdf
file containing your written report and a
.tar
file containing a directory structure with your C or C++ source code. Your grade will be reduced if you do not follow the submission instructions.
All written reports (for both 472 and 572 students) must be composed in MS Word, LaTeX, or some other word processor and submitted as a PDF file.
Please take the time to read this entire document. If you have questions there is a high likelihood that another section of the document provides answers.
Introduction
In this final project you will implement a cache simulator. Your simulator will be configurable and will be able to handle caches with varying capacities, block sizes, levels of associativity, replacement policies, and write policies. The simulator will operate on trace files that indicate memory access properties. All input files to your simulator will follow a specific structure so that you can parse the contents and use the information to set the properties of your simulator.
After execution is finished, your simulator will generate an output file containing information on the number of cache misses, hits, and miss evictions (i.e. the number of block replacements). In addition, the file will also record the total number of (simulated) clock cycles used during the situation. Lastly, the file will indicate how many read and write operations were requested by the CPU.
It is important to note that your simulator is required to make several significant assumptions for the sake of simplicity.
1. You do not have to simulate the actual data contents. We simply pretend that we copied data from main memory and keep track of the hypothetical time that would have elapsed.
2. Accessing a sub-portion of a cache block takes the exact same time as it would require to access the entire block. Imagine that you are working with a cache that uses a 32 byte block size and has an access time of 15 clock cycles. Reading a 32 byte block from this cache will require 15 clock cycles. However, the same amount of time is required to read 1 byte from the cache.
3. In this project assume that main memory RAM is always accessed in units of 8 bytes (i.e. 64 bits at a time).
When accessing main memory, it's expensive to access the first unit. However, DDR memory typically includes buffering which means that the RAM can provide access to the successive memory (in 8 byte chunks) with minimal overhead. In this project we assume an
overhead of 1 additional clock cycle per contiguous unit
.
For example, suppose that it costs 255 clock cycles to access the first unit from main memory. Based on our assumption, it would only cost 257 clock cycles to access 24 bytes of memory.
4. Assume that all caches utilize a "fetch-on-write" scheme if a miss occurs on a Store operation. This means that.
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Interview with Anatoliy Kuznetsov, the author of BitMagic C++ library
1. Interview with Anatoliy Kuznetsov, the
author of BitMagic C++ library
Author: Andrey Karpov
Date: 08.11.2009
Abstract
In this article, Anatoliy Kuznetsov answers the questions and tells us about the open BitMagic C++
Library.
Introduction
While regularly looking through the Internet-resources related to the sphere of 64-bit programming, I
often came across mentioning about BitMagic C++ Library and that it had gained a lot of benefits from
using 64-bits. I decided to communicate with the library's author and offer him to tell us in an interview
about his research and developments.
The questions are asked by: Andrey Karpov - "Program Verification Systems" company's worker
developing PVS-Studio tool for verification of modern C++ applications.
The answers are given by: Anatoliy Kuznetsov - chief software engineer in NCBI; developer of the open
library BitMagic C++ Library.
Hello, Anatoliy. Please, tell us about yourself. What projects are you
involved in?
Hello Andrey,
I am chief software engineer, at present I am working in the team of searching and visualizing bio-
molecular information in NCBI (National Center for Biotechnology Information). Besides my major
activity, I am the chief developer and architect of the open library BitMagic C++ Library.
By education I am planning engineer, a graduate of the Lobachevskiy University in Nizhniy Novgorod.
What is BitMagic?
BitMagic was developed as a universal template library for working with compressed bit vectors. The
library solves several tasks:
• Provides a bit container which is really compatible with STL by ideology. It means that the
container must support iterators, memory allocators and interact with algorithms and other STL
containers.
• The library can efficiently operate very long and sparse vectors.
• Provides a possibility of serialization of vectors for further writing them into databases or
sending by net.
2. • A developer is provided with a set of algorithms for implementing set-theory operations and
calculating distances and similarity metrics in multidimensional binary spaces.
• Much consideration is given to optimization for the popular calculation acceleration systems,
such as SSE.
In case of what tasks to be solved can BitMagic be of most interest for
developers?
The library turned out to be rather universal and perhaps it wouldn't be easy to list all the possible ways
to use it. At present, the library is of most interest in the following spheres:
• Building of bit and inverted indexes for full-text search systems, acceleration of relational
algebra operations (AND, OR, JOIN etc).
• Development of non-standard extensions and indexes for existing databases (Oracle Cartridges,
MS SQL extended stored procedures). As a rule, such extensions help integrate scientific,
geographic and other non-standard data into the database.
• Development of data mining algorithms.
• Development of in-memory indexes and databases.
• Development of systems of precise access differentiation with a large number of objects
(security enhanced databases with differentiation of access to separate fields and columns).
• Task management systems (on the computation cluster), systems of real-time tracing of task
states, storage of task states described as Finite State Machines.
• Tasks of representing and storage of strongly connected graphs.
What can you tell about the history of creating BitMagic library? What
prompted you to create it?
For a long time, I and my colleagues had been working with the tasks related to large databases, analysis
and visualization systems. The very first working version demonstrating bit vectors' abilities was shown
by Maxim Shemanaryov (he is the developer of a wonderful 2D vector graphics library Antigrain
Geometry: http://www.antigrain.com). Then, some ideas of equivalent representation of sets were
described by Koen Van Damm, an engineer from Europe who was working on the parsers of
programming languages for verifying complex systems. There were other sources as well. I decided to
systematize it all somehow and present in the form of a library suitable for multiple use in various
projects.
What are the conditions of BitMagic library's distribution? Where can
one download it?
The library is free for commercial and non-commercial use and is available in the form of source texts.
The only restriction is the demand of mentioning the library and its authors when using it in the finite
product.
You can see the materials here: http://bmagic.sourceforge.net.
3. Am I right supposing that BitMagic gains significant advantages after
being compiled in the 64-bit version?
Really, the library uses a series of optimization methods accelerating work in 64-bit systems or systems
with SIMD commands (128-bit SSE2).
Here are the factors accelerating execution of algorithms:
• a wide machine word (logical operations are performed over a wide word);
• the programmer (and the compiler) has access to additional registers and lack of registers is not
so crucial (there is such a disadvantage in x86 architecture);
• memory alignment often accelerates operation (128-bit alignment of addresses provides a good
result);
• and of course the possibility to place more objects and data being processed in the memory of
one program. This is a great plus of the 64-bit version clear to everyone.
At present, the quickest operation is available when using 128-bit SSE2 optimization in a 64-bit program.
This mode combines the double number of x86 registers and the wide machine word to perform logical
operations.
64-bit systems and programs are going through a real Renaissance. Migration of programs on 64-bits
will be faster than moving from 16 to 32. Appearance of 64-bit versions of Windows on mass market and
available toolkits (like the one your company is developing) will stimulate this process. In the
environment of constant growth of systems' complexity and the size of code used in them, such a toolkit
as PVS-Studio is a good help as it reduces efforts and forces release of products.
Tell us about the compression methods used in BitMagic, please.
The current 3.6.0 version of the library uses several compression methods.
1. "Bitvectors" in memory are split into blocks. If a block is not occupied or is occupied fully, it is
not allocated. That is, the programmer can set bits in a range very far from zero. Setting of bit
100,000,000 doesn't lead to an explosion in memory consumption which is often characteristic
of vectors with two-dimensional linear model.
2. Blocks in memory can have an equivalent representation in the form of areas - gaps. Actually,
this is a kind of RLE coding. Unlike RLE, our library doesn't lose the ability to execute logical
operations or access random bits.
3. When serializing "bitvectors", a set of other methods is used: conversion into lists of integer
numbers (representing nulls or ones) and list coding by Elias Gamma Coding method. When
using these methods, we do lose the ability of random bit access but it is not so crucial for
writing on the disk in comparison with the reduction of costs on storage and input-output.
Could you give some code examples demonstrating the use of BitMagic
library?
One of the examples simply creates 2 vectors, initializes them and performs the logical operation AND.
Further, the class enumerator is used for iteration and printing of the values saved in the vector.
#include <iostream>
4. #include "bm.h"
using namespace std;
int main(void)
{
bm::bvector<> bv;
bv[10] = true; bv[100] = true; bv[10000] = true;
bm::bvector<> bv2(bv);
bv2[10000] = false;
bv &= bv2;
bm::bvector<>::enumerator en = bv.first();
bm::bvector<>::enumerator en_end = bv.end();
for (; en < en_end; ++en) {
cout << *en << endl;
}
return 0;
}
The next example demonstrates serialization of vectors and use of compression mode.
#include <stdlib.h>
#include <iostream>
#include "bm.h"
#include "bmserial.h"
using namespace std;
// This procedure creates very dense bitvector.
// The resulting set will consists mostly from ON (1) bits
// interrupted with small gaps of 0 bits.
//
void fill_bvector(bm::bvector<>* bv)
{
for (unsigned i = 0; i < MAX_VALUE; ++i) {
if (rand() % 2500) {
6. // Allocate serialization buffer.
unsigned char* buf =
new unsigned char[st.max_serialize_mem];
// Serialization to memory.
unsigned len = bvs.serialize(bv, buf, 0);
cout << "Serialized size:" << len << endl << endl;
return buf;
}
int main(void)
{
bm::bvector<> bv1;
bm::bvector<> bv2;
// set DGAP compression mode ON
bv2.set_new_blocks_strat(bm::BM_GAP);
fill_bvector(&bv1);
fill_bvector(&bv2);
// Prepare a serializer class
// for best performance it is best
// to create serilizer once and reuse it
// (saves a lot of memory allocations)
//
bm::serializer<bm::bvector<> > bvs;
// next settings provide lowest serilized size
bvs.byte_order_serialization(false);
bvs.gap_length_serialization(false);
bvs.set_compression_level(4);
unsigned char* buf1 = serialize_bvector(bvs, bv1);
unsigned char* buf2 = serialize_bvector(bvs, bv2);
// Serialized bvectors (buf1 and buf2) now ready to be
// saved to a database, file or send over a network.
7. // ...
// Deserialization.
bm::bvector<> bv3;
// As a result of desrialization bv3
// will contain all bits from
// bv1 and bv3:
// bv3 = bv1 OR bv2
bm::deserialize(bv3, buf1);
bm::deserialize(bv3, buf2);
print_statistics(bv3);
// After a complex operation
// we can try to optimize bv3.
bv3.optimize();
print_statistics(bv3);
delete [] buf1;
delete [] buf2;
return 0;
}
What are your plans on developing BitMagic library?
We wish to implement some new vector compression methods with the ability of parallel data
procession.
Due to mass release of Intel Core i5-i7-i9, it is rational to release the library's version for SSE 4.2. Intel
company added some interesting features which can be efficiently used. The most interesting is the
hardware support of bit number calculation (Population Count).
We are experimenting with nVidia CUDA and other GPGPU. Graphics cards allow you to perform integer
and logical operations today - and their resources can be used for algorithms of working with sets and
compression.
References
1. Elias Gamma encoding of bit-vector Delta gaps (D-Gaps).
http://www.viva64.com/go.php?url=517
2. Hierarchical Compression. http://www.viva64.com/go.php?url=518
3. D-Gap Compression. http://www.viva64.com/go.php?url=519
8. 4. 64-bit Programming And Optimization. http://www.viva64.com/go.php?url=520
5. Optimization of memory allocations. http://www.viva64.com/go.php?url=521
6. Bitvector as a container. http://www.viva64.com/go.php?url=522
7. 128-bit SSE2 optimization. http://www.viva64.com/go.php?url=523
8. Using BM library in memory saving mode. http://www.viva64.com/go.php?url=524
9. Efficient distance metrics. http://www.viva64.com/go.php?url=525