Parallel programming platforms are introduced here. For more information about parallel programming and distributed computing visit,
https://sites.google.com/view/vajira-thambawita/leaning-materials
Parallel programming platforms are introduced here. For more information about parallel programming and distributed computing visit,
https://sites.google.com/view/vajira-thambawita/leaning-materials
Threads,
system model,
processor allocation,
scheduling in distributed systems
Load balancing and
sharing approach,
fault tolerance,
Real time distributed systems,
Process migration and related issues
Virtual Memory
• Copy-on-Write
• Page Replacement
• Allocation of Frames
• Thrashing
• Operating-System Examples
Background
Page Table When Some PagesAre Not in Main Memory
Steps in Handling a Page Fault
Parallel computing is a type of computation in which many calculations or the execution of processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has been employed for many years, mainly in high-performance computing, but interest in it has grown lately due to the physical constraints preventing frequency scaling. As power consumption (and consequently heat generation) by computers has become a concern in recent years, parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multi-core processors.
INTRODUCTIONTO OPERATING SYSTEM
What is an Operating System?
Mainframe Systems
Desktop Systems
Multiprocessor Systems
Distributed Systems
Clustered System
Real -Time Systems
Handheld Systems
Computing Environments
A multiprocessor is a computer system with two or more central processing units (CPUs), with each one sharing the common main memory as well as the peripherals. This helps in simultaneous processing of programs.
The key objective of using a multiprocessor is to boost the system’s execution speed, with other objectives being fault tolerance and application matching.
A good illustration of a multiprocessor is a single central tower attached to two computer systems. A multiprocessor is regarded as a means to improve computing speeds, performance and cost-effectiveness, as well as to provide enhanced availability and reliability.
Caches in multiprocessing environment introduce the Cache Coherence problem.
When multiple processors maintain locally cached copies of a unique shared memory location, any local modification of the location can result in a globally inconsistent view of memory. This is called Cache Coherence Problem.
A brief discussion about its solutions are given.
The theory behind parallel computing is covered here. For more theoretical knowledge: https://sites.google.com/view/vajira-thambawita/leaning-materials
In this presentation, you will learn the fundamentals of Multi Processors and Multi Computers in only a few minutes.
Meanings, features, attributes, applications, and examples of multiprocessors and multi computers.
So, let's get started. If you enjoy this and find the information beneficial, please like and share it with your friends.
Memory management is the act of managing computer memory. The essential requirement of memory management is to provide ways to dynamically allocate portions of memory to programs at their request, and free it for reuse when no longer needed. This is critical to any advanced computer system where more than a single process might be underway at any time
Threads,
system model,
processor allocation,
scheduling in distributed systems
Load balancing and
sharing approach,
fault tolerance,
Real time distributed systems,
Process migration and related issues
Virtual Memory
• Copy-on-Write
• Page Replacement
• Allocation of Frames
• Thrashing
• Operating-System Examples
Background
Page Table When Some PagesAre Not in Main Memory
Steps in Handling a Page Fault
Parallel computing is a type of computation in which many calculations or the execution of processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has been employed for many years, mainly in high-performance computing, but interest in it has grown lately due to the physical constraints preventing frequency scaling. As power consumption (and consequently heat generation) by computers has become a concern in recent years, parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multi-core processors.
INTRODUCTIONTO OPERATING SYSTEM
What is an Operating System?
Mainframe Systems
Desktop Systems
Multiprocessor Systems
Distributed Systems
Clustered System
Real -Time Systems
Handheld Systems
Computing Environments
A multiprocessor is a computer system with two or more central processing units (CPUs), with each one sharing the common main memory as well as the peripherals. This helps in simultaneous processing of programs.
The key objective of using a multiprocessor is to boost the system’s execution speed, with other objectives being fault tolerance and application matching.
A good illustration of a multiprocessor is a single central tower attached to two computer systems. A multiprocessor is regarded as a means to improve computing speeds, performance and cost-effectiveness, as well as to provide enhanced availability and reliability.
Caches in multiprocessing environment introduce the Cache Coherence problem.
When multiple processors maintain locally cached copies of a unique shared memory location, any local modification of the location can result in a globally inconsistent view of memory. This is called Cache Coherence Problem.
A brief discussion about its solutions are given.
The theory behind parallel computing is covered here. For more theoretical knowledge: https://sites.google.com/view/vajira-thambawita/leaning-materials
In this presentation, you will learn the fundamentals of Multi Processors and Multi Computers in only a few minutes.
Meanings, features, attributes, applications, and examples of multiprocessors and multi computers.
So, let's get started. If you enjoy this and find the information beneficial, please like and share it with your friends.
Memory management is the act of managing computer memory. The essential requirement of memory management is to provide ways to dynamically allocate portions of memory to programs at their request, and free it for reuse when no longer needed. This is critical to any advanced computer system where more than a single process might be underway at any time
Conheça as novidades que o KitKat trouxe relacionadas a economia, como isso pode afetar sua aplicação e como você pode ajudar o Android a gastar menos energia
Esta palestra tem como objetivo demonstrar ao desenvolvedor, de forma prática, como a modernização de código traz um ganho de desempenho considerável explorando diferentes níveis de paralelismo (vetorização e multithreading) disponíveis nas arquiteturas multi-core (processadores Core™ e Xeon®) e many-core (co-processador Xeon Phi™). De forma breve, também será abordado nesta palestra temas como “Visão da Intel para computação Exascale” e iniciativas da Intel® em HPC no Brasil.
Companies in today\'s challenging economy need to do more with less...see how the combination of Cisco, NetApp and VMWare can help you in your data center.
Rendering Battlefield 4 with Mantle by Johan Andersson - AMD at GDC14AMD Developer Central
Johan Andersson will show how the Frostbite 3 game engine is using the low-level graphics API Mantle to deliver significantly improved performance in Battlefield 4 on PC and future games from Electronic Arts in this presentation from the 2014 Game Developers Conference in San Francisco March 17-21. Also view this and other presentations on our developer website at http://developer.amd.com/resources/documentation-articles/conference-presentations/
In this technical presentation Johan Andersson shows how the Frostbite 3 game engine is using the low-level graphics API Mantle to deliver significantly improved performance in Battlefield 4 on PC and future games from Electronic Arts. He will go through the work of bringing over an advanced existing engine to an entirely new graphics API, the benefits and concrete details of doing low-level rendering on PC and how it fits into the architecture and rendering systems of Frostbite. Advanced optimization techniques and topics such as parallel dispatch, GPU memory management, multi-GPU rendering, async compute & async DMA will be covered as well as sharing experiences of working with Mantle in general.
Kernel Recipes 2018 - XDP: a new fast and programmable network layer - Jesper...Anne Nicolas
This talk will introduce XDP (eXpress Data Path), and explain how this is essentially a new (programmable) network layer in-front of the existing network stack. Then it will dive into the details of the new XDP redirect feature, which goes beyond forwarding packets out other NIC devices.
The eXpress Data Path (XDP) has been gradually integrated into the Linux kernel over several releases. XDP offers fast and programmable packet processing in kernel context. The operating system kernel itself provides a safe execution environment for custom packet processing applications, in form of eBPF programs, executed in device driver context. XDP provides a fully integrated solution working in concert with the kernel’s networking stack. Applications are written in higher level languages such as C and compiled via LLVM into eBPF bytecode which the kernel statically analyses for safety, and JIT translates into native instructions. This is an alternative approach, compared to kernel bypass mechanisms (like DPDK and netmap).
Asymmetric multi-processing (AMP) systems fulfill the need for high performance and real-time by combining the responsiveness of a MCU with the processing power of an application processor which runs a full OS.
This talk will present a technical overview on asymmetric multiprocessing platforms focussing on motivations, use cases and how to handle interprocess communication between MCU and MPU in practice.
Asymmetric Multiprocessing - Kynetics ELC 2018 portlandNicola La Gloria
Asymmetric multi-processing (AMP) systems fulfill the need for high performance and real-time by combining the responsiveness of a MCU with the processing power of an application processor which runs a full OS.
This talk will present a technical overview on asymmetric multiprocessing platforms focussing on motivations, use cases and how to handle interprocess communication between MCU and MPU in practice.
AVOIDING DUPLICATED COMPUTATION TO IMPROVE THE PERFORMANCE OF PFSP ON CUDA GPUScsandit
Graphics Processing Units (GPUs) have been emerged as powerful parallel compute platforms for various
application domains. A GPU consists of hundreds or even thousands processor cores and adopts Single
Instruction Multiple Threading (SIMT) architecture. Previously, we have proposed an approach that
optimizes the Tabu Search algorithm for solving the Permutation Flowshop Scheduling Problem (PFSP)
on a GPU by using a math function to generate all different permutations, avoiding the need of placing all
the permutations in the global memory. Based on the research result, this paper proposes another
approach that further improves the performance by avoiding duplicated computation among threads,
which is incurred when any two permutations have the same prefix. Experimental results show that the
GPU implementation of our proposed Tabu Search for PFSP runs up to 1.5 times faster than another GPU
implementation proposed by Czapinski and Barnes
Palestra realizada por Luciano Palma no Intel Software Day 2013 (22/10/2013)
Conheça a arquitetura do Intel Xeon Phi, um coprocessador capaz de entregar mais de 2 TFlops de processamento para sua solução de HPC (High Performance Computing).
Across the Silicon Spectrum: Xeon Phi to Quark – Unleash the Performance in Y...Intel Software Brasil
Paul Butler's presentation at Intel Software Day 2013 (10/22/2013)
Learn how to access robust Intel resources (programs, initiatives, content, tools) available to software developers in Brazil supporting their software development life cycle across all platforms (Windows, Linux, Mac/iOS, and Android)
Aprenda como usar dois padrões abertos (ePUB3 e HTML5) para criar livros eletrônicos interativos.
São livros com apps embarcados, tirando o máximo do que a tecnologia pode lhe oferecer hoje para prover uma experiência de leitura, interação e aprendizado compatível com os nativos digitais que temos nas escolas de hoje e de amanhã !
O número crescente de dispositivos móveis e sistemas operacionais que encontramos hoje na indústria, traz aos desenvolvedores um desafio técnico adicional: Como escrever aplicações com o menor custo e maior alcance de audiência ?
Apps híbridos com HTML5 possibilitam aos desenvolvedores manter uma única base de código e gerar a partir dela Apps para diversos dispositivos e sistemas operacionais móveis, com maior flexibilidade e menor time to market.
O Intel XDK New é uma ferramenta gratuita e completa para o desenvolvimento, testes, emulação, depuração e compilação na nuvem de aplicativos híbridos em HTML5.
Apresentação realizada na trilha de educação durante o Intel Software Day 2013. O tema trata das transformações no modelo de educação como o conhecemos e como uma solução educacional pode ajudar nesta tranformação.
2. 2 2
Non-Uniform Memory Access (NUMA)
FSB architecture
- All memory in one location
Starting with Nehalem
- Memory located in multiple
places
Latency to memory
dependent on location
Local memory
- Highest BW
- Lowest latency
Remote Memory
- Higher latency
Socket 0 Socket 1
QPI
Ensure software is NUMA-optimized for best performance
Notes for Intel Software Conference – Brazil, May 2014
3. 3 3
CPU1 DRAM
Node 1
Non-Uniform Memory Access (NUMA)
Locality matters
- Remote memory access latency ~1.7x than local memory
- Local memory bandwidth can be up to 2x greater than remote
Intel® QPI = Intel® QuickPath Interconnect
Remote Memory Access
Intel®
QPI
CPU0DRAM
Local Memory Access
Node 0
BIOS:
- NUMA mode (NUMA Enabled)
First Half of memory space on Node 0, second half on Node 1
Should be default on Nehalem (!)
- Non-NUMA (NUMA Disabled)
Even/Odd cache lines assigned to Nodes 0/1: Line interleaving
Notes for Intel Software Conference – Brazil, May 2014
4. 4 4
Local Memory Access Example
CPU0 requests cache line X, not present in any CPU0 cache
- CPU0 requests data from its DRAM
- CPU0 snoops CPU1 to check if data is present
Step 2:
- DRAM returns data
- CPU1 returns snoop response
Local memory latency is the maximum latency of the two responses
Nehalem optimized to keep key latencies close to each other
CPU0 CPU1
QPI
DRAMDRAM
Notes for Intel Software Conference – Brazil, May 2014
5. 5 5
Remote Memory Access Example
CPU0 requests cache line X, not present in any CPU0 cache
- CPU0 requests data from CPU1
- Request sent over QPI to CPU1
- CPU1’s IMC makes request to its DRAM
- CPU1 snoops internal caches
- Data returned to CPU0 over QPI
Remote memory latency a function of having a low latency
interconnect
CPU0 CPU1
QPI
DRAMDRAM
Notes for Intel Software Conference – Brazil, May 2014
6. 6 6
Non Uniform Memory Access and
Parallel Execution
Process-parallel execution:
- NUMA friendly- data belongs only to the process
- E.g. MPI
- Affinity pinning maximizes local memory access
- Standard for HPC
Shared-memory threading:
- More problematic: same thread may require data from multiple
NUMA nodes
- E.g. OpenMP, TBB , explicit threading
- OS scheduled thread migration can aggravate situation
- NUMA and non-NUMA should be compared
Notes for Intel Software Conference – Brazil, May 2014
7. 7 7
Operating System Differences
Operating systems allocate data differently
Linux*
- Malloc reserves the memory
- Assigns the physical page when data touched (first touch)
Many HPC code initialize memory by single ‘master’ thread !!
- A couple of extensions available via numactl and libnuma like
numactl --interleave=all /bin/program
numactl --cpunodebind=1 --membind=1 /bin/program
numactl --hardware
numa_run_on_node(3) // run thread on node 3
Microsoft Windows*
- Malloc assigns the physical page on allocation
- This default allocation policy is not NUMA friendly
- Microsoft Windows has NUMA Friendly API’s
VirtualAlloc reserves memory (like malloc on Linux*)
Physical pages assigned at first use
For more details:
http://kernel.org/pub/linux/kernel/people/christoph/pmig/numamemory.pdf
http://msdn.microsoft.com/en-us/library/aa363804.aspx
Notes for Intel Software Conference – Brazil, May 2014
8. 8 8
Other Ways to Set Process Affinity
taskset: sets or retrieves the CPU affinity
Intel MPI: using I_MPI_PIN and
I_MPI_PIN_PROCESSOR_LIST environment
variables
KMP_AFFINITY on Intel Compilers OpenMP
- Compact: binds the OpenMP thread n+1 as close as
possible to OpenMP thread n
- Scatter: distributes threads evenly across the entire
system. Scatter is the opposite of compact
Notes for Intel Software Conference – Brazil, May 2014
9. 9 9
NUMA Application Level Tuning:
Shared Memory Threading Example: TRIAD
Parallelized time consuming hotspot “TRIAD” (e.g.
of STREAM benchmark) using OpenMP
main() {
…
#pragma omp parallel
{
//Parallelized TRIAD loop…
#pragma omp parallel for private(j)
for (j=0; j<N; j++)
a[j] = b[j]+scalar*c[j];
} //end omp parallel
…
} //end main
Parallelizing hotspots may not be sufficient for NUMA
Notes for Intel Software Conference – Brazil, May 2014
10. 10 10
NUMA Shared Memory Threading
Example ( Linux* )
KMP_AFFINITY=compact,0,verbose
main() {
…
#pragma omp parallel
{
#pragma omp for private(i)
for(i=0;i<N;i++)
{ a[i] = 10.0; b[i] = 10.0; c[i] = 10.0;}
…
//Parallelized TRIAD loop…
#pragma omp parallel for private(j)
for (j=0; j<N; j++)
a[j] = b[j]+scalar*c[j];
} //end omp parallel …
} //end main
Each thread initializes its data
pinning the pages to local memory
Environment variable
to pin affinity
Same thread that initialized
data uses data
Notes for Intel Software Conference – Brazil, May 2014
11. 11 11
NUMA Optimization Summary
NUMA adds complexity to software parallelization
and optimization
Optimize for latency and for bandwidth
- In most cases goal to minimize latency
- Use local memory
- Keep memory near the thread it accesses
- Keep thread near memory it uses
Rely on quality middle-ware for CPU affinitization:
Example: Intel Compiler OpenMP or MPI environment
variables
Application level tuning may be required to
minimize NUMA first touch policy effects
Notes for Intel Software Conference – Brazil, May 2014
12. 12 12Notes for Intel Software Conference – Brazil, May 2014