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An HPC abstraction over MPI that uses pipes and pydash primitives for composable super-computing.

Project description

Blazer Logo

An HPC abstraction over MPI that uses pipes and pydash primitives. Blazer will handle all the MPI orchestration behind the scenes for you. You just work strictly with data and functions. Easy!


From pypi

$ pip install blazer

From clone

$ git clone
$ cd blazer
$ make init install

NOTE: For some tests ensure you have slurm configured properly (single or muli-machine). However, using slurm is not required to use blazer.


$ make lint


(venv) $ mpirun -n 2 python test
blazer/tests/ PASSED                      [ 50%]
blazer/tests/ PASSED                      [100%]

============================== 2 passed in 0.48s ===============================


$ ./bin/


import blazer
from blazer.hpc.mpi import parallel, pipeline, partial as p, scatter, where, select, filter, rank, size

def calc_some(value, *args):
    """ Do some calculations """
    result = { 'some': value }
    return result

def calc_stuff(value, *args):
    """ Do some calculations """
    result = { 'this': value }
    return result

def add_date(result):
    from datetime import datetime
    if type(result) is dict:
        result['date'] = str(
    return result

def calc_more_stuff(result):
    """ Do some more calculations """
    if type(result) is list:
        result += [{'more':'stuff'}]
    elif type(result) is dict:
        result['more'] = 'stuff'
    return result

INPUT_DATA = 'that'

with blazer.begin():
        p(calc_stuff, 1),
        p(calc_stuff, 2),
        p(calc_stuff, 3),
        p(calc_stuff, 4),
        p(calc_stuff, 5)

    if blazer.ROOT:
        r = list(
            | where(lambda g: where(lambda g: g['this'] > 1))
            | select(lambda g: p(calc_stuff, g['this']*2))
        # Run the composed computation in parallel, wait for result
        result = parallel(r)

        p(calc_stuff, 'DATA'),
        p(pipeline, [

    scatter_data = scatter(list(range(0,(size*2)+2)), calc_some)

    result = pipeline([
        p(calc_stuff, INPUT_DATA), 
                p(parallel, [

    blazer.print("PIPELINE RESULT:",result)

    def get_data():
        """ Data generator """
        for i in range(0,(size*2)):
            yield i

    result = scatter(get_data(), calc_some)

To run:

(venv) $ export PYTHONPATH=.
(venv) $ mpirun -n 4 python blazer/examples/ 
PARALLEL1: [{'this': 1}, {'this': 2}, {'this': 3}, {'this': 4}, {'this': 5}]
PARALLEL2: [{'this': 4}, {'this': 6}, {'this': 2}, {'this': 8}, {'this': 10}]
PIPELINE: {'this': {'some': ({'this': 'DATA'},), 'date': '2022-02-11 02:47:23.356461'}}
SCATTER_DATA: [{'some': 0}, {'some': 1}, {'some': 2}, {'some': 3}, {'some': 4}, {'some': 5}, {'some': 6}, {'some': 7}, {'some': 8}, {'some': 9}, {'some': None}, {'some': None}]
PIPELINE RESULT: [{'this': [{'this': ([{'some': 0}, {'some': 1}, {'some': 2}, {'some': 3}, {'some': 4}, {'some': 5}, {'some': 6}, {'some': 7}, {'some': 8}, {'some': 9}, {'some': None}, {'some': None}],)}, {'some': {'some': [{'some': 0}, {'some': 1}, {'some': 2}, {'some': 3}, {'some': 4}, {'some': 5}, {'some': 6}, {'some': 7}, {'some': 8}, {'some': 9}, {'some': None}, {'some': None}]}}]}, {'some': 'some'}, {'more': 'stuff'}]
[0, 1, 2, 3, 4, 5, 6, 7]
SCATTER: [{'some': 0}, {'some': 1}, {'some': 2}, {'some': 3}, {'some': 4}, {'some': 5}, {'some': 6}, {'some': 7}]

A map/reduce example

import blazer
from blazer.hpc.mpi import map, reduce

def sqr(x):
    return x * x

def add(x, y=0):
    return x+y

with blazer.begin():
    result = map(sqr, [1, 2, 3, 4])

    result = reduce(add, result)


To run:

(venv) $ export PYTHONPATH=.
(venv) $ mpirun -n 4 python blazer/examples/ 
[1, 4, 9, 16]

Streaming Compute

Blazer supports the notion of streaming compute to handle jobs where the data can't fit into memory on a single machine. In this example we implement a map/reduce computation where everything is streaming from the source data through the results without loading all the data into memory.

""" Streaming map/reduce example """
from itertools import groupby
from random import randrange
from typing import Generator

import blazer
from blazer.hpc.mpi import stream

def datagen() -> Generator:
    for i in range(0, 1000):
        r = randrange(2)
        v = randrange(100)
        if r:
            yield {"one": 1, "value": v}
            yield {"zero": 0, "value": v}

def key_func(k):
    return k["key"]

def map(datum):
    datum["key"] = list(datum.keys())[0]
    return datum

def reduce(datalist):
    from blazer.hpc.mpi import rank

    _list = sorted(datalist, key=key_func)
    grouped = groupby(_list, key_func)
    return [{"rank": rank, key: list(group)} for key, group in grouped]

with blazer.begin():
    import json

    mapper = stream(datagen(), map, results=True)
    reducer = stream(mapper, reduce, results=True)
    if blazer.ROOT:
        for result in reducer:
            blazer.print("RESULT", json.dumps(result, indent=4))

NOTE: blazer has (currently) only been tested on mpirun (Open MPI) 4.1.0


Blazer is a high-performance computing (HPC) library that hides the complexities of a super computer's message-passing interface or (MPI). Users want to focus on their code and their data and not fuss with low-level API's for orchestrating results, building pipelines and running fast, parallel code. This is why blazer exists!

With blazer, a user only needs to work with simple, straightforward python. No cumbersome API's, idioms, or decorators are needed. This means they can get started quicker, run faster code, and get their jobs done faster!

General Design

Blazer is designed around the concept of computing primitives or operations. Some of the primitives include:

  • parallel - For computing a list of tasks in parallel
  • pipeline - For computing a list of tasks in sequence, passing the results along
  • map - For mapping a task to a dataset
  • reduce - For mapping a task to a data list and computing a single result

In addition there are other primitives to help manipulate lists of tasks or data, such as:

  • where - Filter a list of tasks or data elements based on a function or lambda
  • select - Apply a function to each list element and return the result

Context Handlers

Blazer uses convenient context handlers to control blocks of code that need to be scheduled to MPI processes behind the scenes. There are two types of context handlers currently.

MPI Context Handler

blazer.begin() is a mandatory context that enables the MPI scheduler behind the various primitives to operate correctly.

import blazer

    def get_data():
        """ Data generator """
        for i in range(0, (size * 2)):
            yield i

    result = scatter(get_data(), calc_some)
    blazer.print("SCATTER:", result)

GPU Context Handler

blazer.gpu() is a context that requests (from the invisible MPI scheduler) dedicated access to a specific GPU on your MPI node fabric.

import logging
import blazer
import numpy as np

from blazer.hpc.mpi.primitives import host, rank
from numba import vectorize
from timeit import default_timer as timer

def dovectors():

    @vectorize(['float32(float32, float32)'], target='cuda')
    def dopow(a, b):
        return a ** b

    vec_size = 100

    a = b = np.array(np.random.sample(vec_size), dtype=np.float32)
    c = np.zeros(vec_size, dtype=np.float32)

    start = timer()
    dopow(a, b)
    duration = timer() - start
    return duration

with blazer.begin(gpu=True):  # on-fabric MPI scheduler
    with blazer.gpu() as gpu:  # on-metal GPU scheduler
        # gpu object contains metadata about the GPU assigned

Shared Data Context Handler

Blazer supports synchronizing shared data across ranks seamlessly. Here is an example of sharing a dictionary where each rank adds its own data to the dictionary and it is available to all other ranks magically!

from random import randrange

import blazer
from blazer.hpc.mpi.primitives import rank

with blazer.environment() as vars:
    rv = randrange(10)
    vars["rank" + str(rank)] = [
        {"key": randrange(10)},

    print("RANK:", rank, "DATA", vars.vars)


Cross-Cluster Supercomputing

Blazer comes with a built-in design pattern for performing cross-cluster HPC. This is useful if you want to allocate compute resources on different super-computers and then build a pipeline of jobs across them. Here is a simple example using ALCF's Cooley and Theta systems (which are built into blazer).

from blazer.hpc.alcf import cooley, thetagpu
from blazer.hpc.local import parallel, pipeline, partial as p

# Log into each cluster using MFA password from MobilePASS
cooleyjob   = cooley.job(user='dgovoni', n=1, q="debug", A="datascience", password=True, script="/home/dgovoni/git/blazer/").login()       
thetajob    = thetagpu.job(user='dgovoni', n=1, q="single-gpu", A="datascience", password=True, script="/home/dgovoni/git/blazer/").login()

def hello(data, *args):
    return "Hello "+str(data)

# Mix and match cluster compute jobs with local code tasks
# in serial chaining
cooleyjob("some data").then(hello).then(thetajob).then(hello)

# Run a cross cluster compute job
result = pipeline([
    p(thetajob,"some data2"),
    p(cooleyjob,"some data1")


When each job .login() method is run, it will gather the MFA login credentials for that system and then use that to schedule jobs on that system via ssh.

Notice the use of the pipeline primitive above. It's the same primitive you would use to build your compute workflows! Composable tasks and composable super-computers.

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