OSX | Linux | Node 6.x, 5.x, 4.x, 0.10, iojs, Python2/3:

API Documentation | EMS Website

Extended Memory Semantics (EMS)

EMS makes possible shared memory parallelism between Node.js, Python, and C/C++.

Extended Memory Semantics (EMS) is a unified programming and execution model that addresses several challenges of parallel programming:

Allows any number or kind of processes to share objects
Manages synchronization and object coherency
Implements persistence to NVM and secondary storage
Provides dynamic load-balancing between processes
May substitute or complement other forms of parallelism

[Parallel Execution Models Supported](#Types\ of\ Concurrency)
[Atomic Operations](#Built-in\ Atomic\ Operations)
Examples
[Benchmarks](#Examples\ and\ Benchmarks)
[Synchronization as a Property of the Data, Not a Duty for Tasks](Synchronization\ as\ a\ Property\ of\ the\ Data,\ Not\ a\ Duty\ for\ Tasks)
Installation
Roadmap

EMS is targeted at tasks too large for one core or one process

A modern multicore server has 16-32 cores and over 200GB of memory, equivalent to an entire rack of systems from a few years ago. As a consequence, jobs formerly requiring a Map-Reduce cluster can now be performed entirely in shared memory on a single server without using distributed programming.

Sharing Persistent Objects Between Python and Javascript

Inter-language example in interlanguage.{js,py}

Start Node.js REPL, create an EMS memory
Store "Hello"
Open a second session, begin the Python REPL
Connect to the EMS shared memory from Python
Show the object created by JS is present
Modify the object, and show the modification can be seen in JS
Exit both REPLs so no programs are running to "own" the EMS memory
Restart Python, show the memory is still present
Initialize a counter from Python
Demonstrate atomic Fetch and Add in JS
Start a loop in Python incrementing the counter
Simultaneously print and modify the value from JS
Try to read "empty" data from Python, process blocks
Write the empty memory, marking it full, Python resumes execution

Types of Concurrency

EMS extends application capabilities to include transactional memory and other fine-grained synchronization capabilities.

EMS implements several different parallel execution models:

Fork-Join Multiprocess: execution begins with a single process that creates new processes when needed, those processes then wait for each other to complete.
Bulk Synchronous Parallel: execution begins with each process starting the program at the main entry point and executing all the statements
User Defined: parallelism may include ad-hoc processes and mixed-language applications

Built-in Atomic Operations

EMS operations may performed using any JSON data type, read-modify-write operations may use any combination of JSON data types, producing identical results to like operations on ordinary data.

All basic and atomic read-modify-write operations are available in all concurrency modes, however collectives are not currently available in user defined modes.

Basic Operations: Read, write, readers-writer lock, read full/empty, write empty/full
Primitives: Stacks, queues, transactions
Atomic Read-Modify-Write: Fetch-and-Add, Compare and Swap
Collective Operations: All basic OpenMP collective operations are implemented in EMS: dynamic, block, guided, and static loop scheduling, barriers, master and single execution regions

Examples and Benchmarks

Word Counting Using Atomic Operations

Map-Reduce is often demonstrated using word counting because each document can be processed in parallel, and the results of each document's dictionary reduced into a single dictionary. This EMS implementation also iterates over documents in parallel, but it maintains a single shared dictionary across processes, atomically incrementing the count of each word found. The final word counts are sorted and the most frequently appearing words are printed with their counts.

The performance of this program was measured using an Amazon EC2 instance:
c4.8xlarge (132 ECUs, 36 vCPUs, 2.9 GHz, Intel Xeon E5-2666v3, 60 GiB memory The leveling of scaling aroung 16 cores despite the presence of ample work may be related to the use of non-dedicated hardware: Half of the 36 vCPUs are presumably HyperThreads or otherwise shared resoruce. AWS instances are also bandwidth limited to EBS storage, where our Gutenberg corpus is stored.

Bandwidth Benchmarking

A benchmark similar to STREAMS gives us the maximum speed EMS double precision floating point operations can be performed on a c4.8xlarge (132 ECUs, 36 vCPUs, 2.9 GHz, Intel Xeon E5-2666v3, 60 GiB memory.

Benchmarking of Transactions and Work Queues

The micro-benchmarked raw transactional performance and performance in the context of a workload are measured separately. The experiments were run using an Amazon EC2 instance:
c4.8xlarge (132 ECUs, 36 vCPUs, 2.9 GHz, Intel Xeon E5-2666v3, 60 GiB memory

Experiment Design

Six EMS arrays are created, each holding 1,000,000 numbers. During the benchmark, 1,000,000 transactions are performed, each transaction involves 1-5 randomly selected elements of randomly selected EMS arrays. The transaction reads all the elements and performs a read-modify-write operation involving at least 80% of the elements. After all the transactions are complete, the array elements are checked to confirm all the operations have occurred.

The parallel process scheduling model used is block dynamic (the default), where each process is responsible for successively smaller blocks of iterations. The execution model is bulk synchronous parallel, each processes enters the program at the same main entry point and executes all the statements in the program. forEach loops have their normal semantics of performing all iterations, parForEach loops are distributed across threads, each process executing only a portion of the total iteration space.

Immediate Transactions: Each process generates a transaction on integer data then immediately performs it.	Transactions from a Queue: One of the processes generates the individual transactions and appends them to a work queue the other threads get work from. Note: As the number of processes increases, the process generating the transactions and appending them to the work queue is starved out by processes performing transactions, naturally maximizing the data access rate.
Immediate Transactions on Strings: Each process generates a transaction appending to a string, and then immediately performs the transaction.	Measurements Elem. Ref'd: Total number of elements read and/or written Table Updates: Number of different EMS arrays (tables) written to Trans. Performed: Number of transactions performed across all EMS arrays (tables) Trans. Enqueued: Rate transactions are added to the work queue (only 1 generator thread in these experiments)

Synchronization as a Property of the Data, Not a Duty for Tasks

EMS internally stores tags that are used for synchronization of user data, allowing synchronization to happen independently of the number or kind of processes accessing the data. The tags can be thought of as being in one of three states, Empty, Full, or Read-Only, and the EMS intrinsic functions enforce atomic access through automatic state transitions.

The EMS array may be indexed directly using an integer, or using a key-index mapping from any primitive type. When a map is used, the key and data itself are updated atomically.

EMS memory is an array of JSON primitive values (Number, Boolean, String, or Undefined) accessed using atomic operators and/or transactional memory. Safe parallel access is managed by passing through multiple gates: First mapping a key to an index, then accessing user data protected by EMS tags, and completing the whole operation atomically.

EMS Data Tag Transitions & Atomic operations: F=Full, E=Empty, X=Don't Care, RW=Readers-Writer lock (# of current readers) CAS=Compare-and-Swap, FAA=Fetch-and-Add

More Technical Information

For a more complete description of the principles of operation, visit the EMS web site.

Complete API reference

Installation

Because all systems are already multicore, parallel programs require no additional equipment, system permissions, or application services, making it easy to get started. The reduced complexity of lightweight threads communicating through shared memory is reflected in a rapid code-debug cycle for ad-hoc application development.

Quick Start with the Makefile

To build and test all C, Python 2 and 3, and Node.js targets, a makefile can automate most build and test tasks.

dunlin> make help
         Extended Memory Semantics  --  Build Targets
===========================================================
    all                       Build all targets, run all tests
    node                      Build only Node.js
    py                        Build both Python 2 and 3

    py[2|3]                   Build only Python2 or 3
    test                      Run both Node.js and Py tests
    test[_js|_py|_py2|_py3]   Run only Node.js, or only Py tests, respectively
    clean                     Remove all files that can be regenerated
    clean[_js|_py|_py2|_py3]  Remove Node.js or Py files that can be regenerated

Install via npm

EMS is available as a NPM Package. EMS itself has no external dependencies, but does require compiling native C++ functions using node-gyp, which is also available as a NPM (sudo npm install -g node-gyp).

The native C parts of EMS depend on other NPM packages to compile and load. Specifically, the Foreign Function Interface (ffi), C-to-V8 symbol renaming (bindings), and the native addon abstraction layer (nan) are also required to compile EMS.

npm install ems

Install via GitHub

Download the source code, then compile the native code:

git clone https://github.com/SyntheticSemantics/ems.git
cd ems
npm install

To use this EMS development build to run the examples or tests, set up a global npm link to the current build:

sudo npm link ../ems

Run Some Examples

On a Mac and most Linux distributions EMS will "just work", but some Linux distributions restrict access to shared memory. The quick workaround is to run jobs as root, a long-term solution will vary with Linux distribution.

Run the work queue driven transaction processing example on 8 processes:

npm run <example>

Or manually via:

cd Examples
node concurrent_Q_and_TM.js 8

Running all the tests with 8 processes:

npm run test      # Alternatively: npm test

cd Tests
rm -f EMSthreadStub.js   # Do not run the machine generated script used by EMS
for test in `ls *js`; do node $test 8; done

Platforms Supported

As of 2016-05-01, Mac/Darwin and Linux are supported. A windows port pull request is welcomed!

Roadmap

EMS 1.0 uses Nan for long-term Node.js support, we continue to develop on OSX and Linux via Vagrant.

EMS 1.3 introduces a C API.

EMS 1.4 Python API

EMS 1.5 [Planned] Support for persistent main system memory.

EMS 2.0 [Planned] New API with more tightly integrate with ES6, Python, and other dynamically typed languages languages, making atomic operations on persistent memory more transparent.

License

BSD, other commercial and open source licenses are available.

Links

Visit the EMS web site

Download the NPM

Get the source at GitHub

About

Jace A Mogill specializes in FPGA/Software Co-Design, recently embedding a FPGA emulation of an ASIC into Python and also designing an hardware accelerator for Python, Javascript, and other languages. He has over 20 years experience optimizing software for distributed, multi-core, and hybrid computer architectures. He regularly responds to [email protected].

zectbynmo / ems Goto Github PK

ems's Introduction