Persistent Shared Memory and Parallel Programming Model
EMS makes possible shared memory parallelism in Node.js (and soon Python).
Extended Memory Semantics (EMS) is a unified programming and execution model that addresses several challenges of parallel programming:
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.
EMS extends application capabilities to include transactional memory and
other fine-grained synchronization capabilities.
EMS implements several different parallel execution models:
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
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.
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.
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
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.
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)
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
For a more complete description of the principles of operation, visit the EMS web site.
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.
EMS is available as a NPM Package. EMS itself has no external dependencies,
but does require compiling native C++ functions using
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
Download the source code, then compile the native code:
git clone https://github.com/SyntheticSemantics/ems.gitcd emsnpm 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
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 Examplesnode concurrent_Q_and_TM.js 8
Running all the tests with 8 processes:
npm run test # Alternatively: npm test
cd Testsrm -f EMSthreadStub.js # Do not run the machine generated script used by EMS
As of 2016-05-01, Mac/Darwin and Linux are supported. A windows port pull request is welcomed!
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 [Planned] Python API
EMS 1.5 [Planned] Support for persistent 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.
BSD, other commercial and open source licenses are available.