In this presentation, I will be introducing to Swift parallel scripting language. I do also demonstrate some hands on examples also.

1. Prof N.B. Venkateswarlu
GVPCEW, Visakhapatnam
venkatritch@gmail.com

2. First Let me thank the organizers,
especially Prof MHM Krishna Prasad
Garu(For whom I am an informal
Ph.D guide/mentor).

3. 3
Outline
• Introduction
• Swift Language Introduction (Swift/K and
Swift/T)
• Swift Coding examples illustrated
Apple Swift is
different.

4. As the theme of the workshop is
research guidance, I would like to
recall my case while I am doing my
PhD

5. Community Clusters – Do You Want to
join me. It is not the place to talk
more. You can be in touch with me at
venkatritch@gmail.com

6. Being an old CSE teacher, I used to ask
students how Computer is used during
1970s and 1980s. Of course, today it is
used mostly for entertainment. Any
one would like to share?.

7. In the same sense, how really the High
Performance Computing is becoming
necessary today and coming years.
An example, Weka.

8. Sunway TaihuLight - Sunway MPP,
Sunway SW26010 260C 1.45GHz,
Sunway, 93Peta Flops
8

9. http://www.journals.elsevier.com/
parallel-computing/most-cited-
articles
9

10. The Scientific Computing Campaign
• Swift addresses most of these components
10
THINK
about what
to run next
RUN a
battery
of tasks
COLLECT
results
IMPROVE
methods
and
codes

11. http://www.scidacreview.org/1002/ht
ml/swift.html

12. http://swift-lang.org/main/
http://swift-lang.org/tryswift/

13. Swift
13
Swift is a data-flow oriented coarse grained scripting
language that supports dataset typing and mapping,
dataset iteration, conditional branching, and procedural
composition.
Swift programs (or workflows) are written in a language
called Swift.
Swift scripts are primarily concerned with processing
(possibly large) collections of data files, by invoking
programs to do that processing. Swift handles execution
of such programs on remote sites by choosing sites,
handling the staging of input and output files to and from
the chosen sites and remote execution of programs.

14. 14
• Swift is a parallel scripting language for multi-cores, clusters,
grids, clouds, and supercomputers
– for loosely-coupled applications - application linked by exchanging files
– debug on a laptop, then run on a Cray
• Swift scripts are easy to write
– simple high-level functional language with C-like syntax
– small Swift scripts can do large-scale work
• Swift is easy to run: contain all services for running workflow - in
one Java application
– simple command line interface
• Swift is fast: based on an efficient execution engine
– scales readily to millions of tasks
• Swift usage is growing:
– applications in neuroscience, proteomics, molecular dynamics,
biochemistry, economics, statistics, earth systems science, and more.

15. 15
Clouds: Magellan,
Amazon,
FutureGrid,
BioNimbus
Challenge:
complex variety of
computing resources
• Parallel distributed computing is
HARD
• Swift harnesses diverse resources
with simple scripts
• Many applications are well suited to
this approach
• Motivates collaboration through
libraries of pipelines and sharing of
data and provenance
Challenge: Complexity of parallel computing

16. 16
When do you need Swift?
Typical application: protein-ligand docking for drug screening
2M+ ligands
(B)
O(100K)
drug
candidates
Tens of fruitful
candidates for
wetlab & APS
O(10)
proteins
implicated
in a disease
1M
compute
jobs
X …
T1af7 T1r69T1b72

17. 17
Submit host
(Laptop, Linux login node…)
Workflow
status
and logs
Java application
Compute
nodes
f1
f2
f3
a1
a2
Data server
f1 f2 f3
Provenance
log
script
App
a1
App
a2
site
list
app
list
File
transport
Swift supports clusters, grids, and supercomputers.
Solution: parallel scripting for high level parallellism

18. Goals of the Swift language
Swift was designed to handle many aspects of
the computing campaign
• Ability to integrate many application components into a new workflow
application
• Data structures for complex data organization
• Portability- separate site-specific configuration from application logic
• Logging, provenance, and
plotting features
• Today, we will focus on supporting multiple
language applications at large scale
18
THIN
K
RU
N
COLL
ECT
IMPR
OVE

19. 19
A Summary of Swift in a nutshell
•Swift scripts are text files ending in .swift The swift command runs on any host, and executes these scripts.
• swift is a Java application, which you can install almost anywhere. On Linux, just unpack
•the distribution tar file and add its bin/ directory to your PATH.
•Swift scripts run ordinary applications, just like shell scripts do.
•Swift makes it easy to run these applications on parallel and remote computers
(from laptops to supercomputers). If you can ssh to the system, Swift can likely run
applications there.
•The details of where to run applications and how to get files back and forth are
described in configuration files separate from your program. Swift speaks ssh, PBS,
Condor, SLURM, LSF, SGE, Cobalt, and Globus to run applications, and scp, http, ftp,
and GridFTP to move data.
•The Swift language has 5 main data types: boolean, int, string, float, and file.
Collections of these are dynamic, sparse arrays of arbitrary dimension and structures of
scalars and/or arrays defined by thetype declaration.
•Swift file variables are "mapped" to external files.
• Swift sends files to and from remote systems for you automatically.
•Swift variables are "single assignment": once you set them you can not change them
(in a given block of code). This makes Swift a natural, "parallel data flow" language.
This programming model keeps your workflow scripts simple and easy to write and
understand.

20. 20
A Summary of Swift in a nutshell
Swift scripts are text files ending in .swift The swift command runs on any host, and executes these scripts.
• This programming model keeps your workflow scripts simple and easy to write and
•understand.
•Swift lets you define functions to "wrap" application programs, and to cleanly
structure more complex scripts.
Swift app functions take files and parameters as inputs and return files as outputs.
•A compact set of built-in functions for string and file manipulation, type conversions,
•high level IO, etc. is provided.
•Swift’s equivalent of printf() is tracef(), with limited and slightly different format codes.
•Swift’s parallel foreach {} statement is the workhorse of the language, and executes all
•iterations of the loop concurrently. The actual number of parallel tasks executed is based
•on available resources and settable "throttles".
•Swift conceptually executes all the statements, expressions and function calls in your
• program in parallel, based on data flow. These are similarly throttled based on available
•resources and settings.
•Swift also has if and switch statements for conditional execution. These are seldom needed
•in simple workflows but they enable very dynamic workflow patterns to be specified.

21. Swift programming model:
all progress driven by concurrent
dataflow
• F() and G() implemented in native code or external programs
• F() and G()run in concurrently in different processes
• r is computed when they are both done
• This parallelism is automatic
• Works recursively throughout the program’s call graph
21
(int r) myproc (int i, int j)
{
int x = F(i);
int y = G(j);
r = x + y;
}

22. 22
Data-flow driven execution
1 (int r) myproc (int i)
2 {
3 j = f(i);
4 k = g(i);
5 r = j + k;
6 }
7 f() and g() are computed in parallel
8 myproc() returns r when they are done
9 This parallelism is AUTOMATIC
10 Works recursively down the program’s call graph
22
F() G()
i
+
j k
r

23. http://csg.csail.mit.edu/Users/arvind/
23
Aravind

24. Swift programming model
• Data types
int i = 4;
string s = "hello world";
file image<"snapshot.jpg">;
• Shell access
app (file o) myapp(file f, int i)
{ mysim "-s" i @f @o; }
• Structured data
typedef image file;
image A[];
type protein_run {
file pdb_in; file sim_out;
}
bag<blob>[] B;
24
 Conventional expressions
if (x == 3) {
y = x+2;
s = strcat("y: ", y);
}
 Parallel loops
foreach f,i in A {
B[i] =
convert(A[i]);
}
 Data flow
merge(analyze(B[0],
B[1]),
analyze(B[2],
B[3]));
Swift: A language for distributed parallel scripting, J. Parallel
Computing, 2011

25. Hierarchical programming model
25
Top-level dataflow script
user-workflow.swift
Distributed dataflow evaluation
Data dependency resolution
Work stealing / load balancing
SWIG-generated wrappers
C C++ Fortran
User libraries
MPI
MPI?

26. Support calls to embedded
interpreters
26
We have
plugins Tcl,
Julia, and
QtScript

27. 27
A simple Swift script: functions run programs
1 type image; // Declare a “file” type.
2
3 app (image output) rotate (image input) {
4 {
5 convert "-rotate" 180 @input @output ;
6 }
7
8 image oldimg <"orion.2008.0117.jpg">;
9 image newimg <"output.jpg">;
10
11 newimg = rotate(oldimg); // runs the “convert” app
27

28. 28
A simple Swift script: functions run programs
1 type image; // Declare a “file” type.
2
3 app (image output) rotate (image input) {
4 {
5 convert "-rotate" 180 @input @output ;
6 }
7
8 image oldimg <"orion.2008.0117.jpg">;
9 image newimg <"output.jpg">;
10
11 newimg = rotate(oldimg); // runs the “convert” app
28
“application”
wrapping function

29. 29
A simple Swift script: functions run programs
1 type image; // Declare a “file” type.
2
3 app (image output) rotate (image input) {
4 {
5 convert "-rotate" 180 @input @output ;
6 }
7
8 image oldimg <"orion.2008.0117.jpg">;
9 image newimg <"output.jpg">;
10
11 newimg = rotate(oldimg); // runs the “convert” app
29
Input fileOutput file
Actual files
to use

30. 30
A simple Swift script: functions run programs
1 type image; // Declare a “file” type.
2
3 app (image output) rotate (image input) {
4 {
5 convert "-rotate" 180 @input @output ;
6 }
7
8 image oldimg <"orion.2008.0117.jpg">;
9 image newimg <"output.jpg">;
10
11 newimg = rotate(oldimg); // runs the “convert” app
30
Invoke the “rotate”
function to run the
“convert” application

31. 31
Parallelism via foreach { }
1 type image;
2
3 (image output) flip(image input) {
4 app {
5 convert "-rotate" "180" @input @output;
6 }
7 }
8
9 image observations[ ] <simple_mapper; prefix=“orion”>;
10 image flipped[ ] <simple_mapper; prefix=“flipped”>;
11
12
13
14 foreach obs,i in observations {
15 flipped[i] = flip(obs);
16 }
31
Name outputs based on index
Process all dataset members in parallel
Map inputs from local directory

32. 32
foreach sim in [1:1000] {
(structure[sim], log[sim]) = predict(p, 100., 25.);
}
result = analyze(structure)
…
1000
Runs of the
“predict”
application
Analyze()
T1af
7
T1r6
9
T1b72
Large scale parallelization with simple loops

33. 33
Nested loops generate massive parallelism
1. Sweep( )
2. {
3. int nSim = 1000;
4. int maxRounds = 3;
5. Protein pSet[ ] <ext; exec="Protein.map">;
6. float startTemp[ ] = [ 100.0, 200.0 ];
7. float delT[ ] = [ 1.0, 1.5, 2.0, 5.0, 10.0 ];
8.
9. foreach p, pn in pSet {
10. foreach t in startTemp {
11. foreach d in delT {
12. ItFix(p, nSim, maxRounds, t, d);
13. }
14. }
15. }
16. }
17. 33
10 proteins x 1000 simulations x
3 rounds x 2 temps x 5 deltas
= 300K tasks

34. 34
A more complex workflow: fMRI data processing
reorientRun
reorientRun
reslice_warpRun
random_select
alignlinearRun
resliceRun
softmean
alignlinear
combinewarp
strictmean
gsmoothRun
binarize
reorient/01
reorient/02
reslice_warp/22
alignlinear/03 alignlinear/07alignlinear/11
reorient/05
reorient/06
reslice_warp/23
reorient/09
reorient/10
reslice_warp/24
reorient/25
reorient/51
reslice_warp/26
reorient/27
reorient/52
reslice_warp/28
reorient/29
reorient/53
reslice_warp/30
reorient/31
reorient/54
reslice_warp/32
reorient/33
reorient/55
reslice_warp/34
reorient/35
reorient/56
reslice_warp/36
reorient/37
reorient/57
reslice_warp/38
reslice/04 reslice/08reslice/12
gsmooth/41
strictmean/39
gsmooth/42gsmooth/43gsmooth/44 gsmooth/45 gsmooth/46 gsmooth/47 gsmooth/48 gsmooth/49 gsmooth/50
softmean/13
alignlinear/17
combinewarp/21
binarize/40
reorient
reorient
alignlinear
reslice
softmean
alignlinear
combine_warp
reslice_warp
strictmean
binarize
gsmooth
Dataset-level Expanded (10 volume) workflow

35. 35
Complex parallel workflows can be easily expressed
Example fMRI preprocessing script below is automatically parallelized
(Run snr) functional ( Run r, NormAnat a,
Air shrink )
{ Run yroRun = reorientRun( r , "y" );
Run roRun = reorientRun( yroRun , "x" );
Volume std = roRun[0];
Run rndr = random_select( roRun, 0.1 );
AirVector rndAirVec = align_linearRun( rndr, std, 12, 1000, 1000, "81 3 3" );
Run reslicedRndr = resliceRun( rndr, rndAirVec, "o", "k" );
Volume meanRand = softmean( reslicedRndr, "y", "null" );
Air mnQAAir = alignlinear( a.nHires, meanRand, 6, 1000, 4, "81 3 3" );
Warp boldNormWarp = combinewarp( shrink, a.aWarp, mnQAAir );
Run nr = reslice_warp_run( boldNormWarp, roRun );
Volume meanAll = strictmean( nr, "y", "null" )
Volume boldMask = binarize( meanAll, "y" );
snr = gsmoothRun( nr, boldMask, "6 6 6" );
}
(Run or) reorientRun (Run ir,
string direction) {
foreach Volume iv, i in ir.v {
or.v[i] = reorient(iv, direction);
}
}

36. 36
SLIM
36

37. 37
SLIM: Swift sketch
type DataFile;
type ModelFile;
type FloatFile;
// Function declaration
app (DataFile[] dFiles) split(DataFile inputFile, int noOfPartition) {
}
app (ModelFile newModel, FloatFile newParameter) firstStage(DataFile data, ModelFile model) {
}
app (ModelFile newModel, FloatFile newParameter) reduce(ModelFile[] model, FloatFile[]
parameter){
}
app (ModelFile newModel) combine(ModelFile reducedModel, FloatFile reducedParameter,
ModelFile oldModel){
}
app (int terminate) check(ModelFile finalModel){
}

38. 38
SLIM: Swift sketch
// Variables to hold the input data
DataFile inputData <"MyData.dat">;
ModelFile earthModel <"MyModel.mdl">;
// Variables to hold the finalized models
ModelFile model[];
// Variables for reduce stage
ModelFile reducedModel[];
FloatFile reducedFloatParameter[];
// Get the number of partition from command line parameter
int n = @toint(@arg("n","1"));
model[0] = earthModel;
//Partition the input data
DataFile partitionedDataFiles[] = split(inputData, n);

39. 39
SLIM: Swift sketch
// Iterate sequentially until the terminal condition is met
iterate v {
// Variables to hold the output of the first stage
ModelFile firstStageModel[] < simple_mapper; location = "output",
prefix = @strcat(v, "_"), suffix = ".mdl" >;
file_dat firstStageFloatParameter[] < simple_mapper; padding = 3, location = "output",
prefix = @strcat(v, "_"), suffix = ".float" >;
// Parallel for loop
foreach partitionedFile, count in partitionedDataFiles {
// First stage
(firstStageModel[count], firstStageFloatParameter[count]) = firstStage(model[v] , partitionedFile);
}
// Reduce stage (use the files for synchronization)
(reducedModel[v], reducedFloatParameter[v]) = reduce(firstStageModel, firstStageFloatParameter);
// Combine stage
model[v+1] = combine(reducedModel[v] ,reducedFloatParameter[v], model[v]);
//Check the termination condition here
int shouldTerminate = check(model[v+1]);
} until (shouldTerminate != 1);

40. 40
Software stack for Swift on ALCF BG/Ps
Swift:
scripting language, task coordination,
throttling, data management, restart
Coaster execution provider:
per-node agents for fast task dispatch
across all nodes
Linux OS
complete, high performance Linux
compute node OS with full fork/exec
Swift
scripts
Shell
scripts
App
invocations
File System

41. 41
Java application
File System
f1 f2 f3
Running Swift scripts
Coaster
Service
Coaster Workers
• Start Coaster
– Start coaster service
– Start coaster workers
(qsub, ssh, …etc)
• Run Swift script
Head node

42. 42
Swift vs. MapReduce
What to compare (to maintain the level of abstraction)?
• Swift framework (language, compiler, runtime, scheduler plugin
– Coasters, storage)
• Map-reduce framework (Hive, Hadoop, HDFS)
Similarities:
• Goals: productive programming environment, hide
complexities related to parallel execution
• able to scale, hide individual failures, load balancing
42

43. 43
Swift vs. MapReduce
Swift Advantages:
• Intuitive programing model.
• Platform independent: from small clusters, to large
ones, to large ‘exotic’ architectures – BG/P, distributed
resources (grids).
- Map/reduce modes will not support all these
- Can generally use the software stack available on the target
machine with no changes
• More flexible data model (just your old files);
- No migration pain;
- Easy to share with other applications
• Optimized for coarse-granularity workflows.
- E.g., files staged in a in-memory file-system; optimizations for
specific patterns.
43

44. • Write site-independent scripts
• Automatic parallelization and data movement
• Run native code, script fragments as applications
• Rapidly subdivide large partitions for
MPI jobs
• Move work to data locations
44
Swift
control
process
Swift
control
process
Swift/T
control
process
Swift worker
process
C
C+
+
Fortr
an
C
C+
+
Fortr
an
C
C+
+
Fortr
an
MPI
Swift/T worker
64K cores of Blue Waters
2 billion Python tasks
14 million Pythons/s
Swift/T: Enabling high-performance
workflows

45. Using Swift/Python/Numpy
global const string numpy = "from numpy import *nn";
typedef matrix string;
(matrix A) eye(int n) {
command = sprintf("repr(eye(%i))", n);
code = numpy+command;
matrix t = python(code);
A = replace_all(t, "n", "", 0);
}
(matrix R) add(matrix A1, matrix A2) {
command = sprintf("repr(%s+%s)", A1, A2);
code = numpy+command;
matrix t = python(code);
R = replace_all(t, "n", "", 0);
}
45
a1 = eye(3);
a2 = eye(3);
sum = add(a1, a2);
printf("2*eye(3)=%s", sum);

46. Fully parallel evaluation of complex
scripts
46
int X = 100, Y = 100;
int A[][];
int B[];
foreach x in [0:X-1] {
foreach y in [0:Y-1] {
if (check(x, y)) {
A[x][y] = g(f(x), f(y));
} else {
A[x][y] = 0;
}
}
B[x] = sum(A[x]);
}

47. Centralized evaluation can be a bottleneck
at extreme scales
47
Had this (Swift/K): For extreme scale, we need (Swift/T):

48. MPI: The Message Passing
Interface• Programming model used on large supercomputers
• Can run on many networks, including sockets, or
shared memory
• Standard API for C and Fortran, other languages have
working implementations
• Contains communication calls for
– Point-to-point (send/recv)
– Collectives (broadcast, reduce, etc.)
• Interesting concepts
– Communicators: collections of
communicating processing and
a context
– Data types: Language-independent
data marshaling scheme
48

49. ADLB: Asynchronous Dynamic Load
Balancer• An MPI library for master-worker
workloads in C
• Uses a variable-size, scalable
network of servers
• Servers implement
work-stealing
• The work unit is a byte array
• Optional work priorities, targets, types
• For Swift/T, we added:
– Server-stored data
– Data-dependent execution
– Tcl bindings!
49
Serv
ers
Workers
• Lusk et al. More scalability, less pain:
A simple programming model and its
implementation for extreme

50. One Swift server core per
node:
16node
job
Flexible placement of server ranks in
a Swift/T job
Four Swift nodes for the job:
16node
job

51. A[3] = g(A[2]);
Example distributed execution
• Code
• Evaluate dataflow operations
• Workers: execute tasks
51
A[2] = f(getenv(“N”));
• Perform getenv()
• Submit f
• Process f
• Store A[2]
• Subscribe to
A[2]
• Submit g
• Process g
• Store A[3]
Task put Task put
• Wozniak et al. Turbine: A distributed-memory dataflow
engine for high performance many-task
applications. Fundamenta Informaticae 128(3), 2013
Task get Task get

52. Swift/T-specific features
• Task locality: Ability to send a task to a process
– Allows for big data –type applications
– Allows for stateful objects to remain resident in
the workflow
– location L = find_data(D);
int y = @location=L f(D, x);
• Data broadcast
• Task priorities: Ability to set task priority
– Useful for tweaking load balancing
• Updateable variables
– Allow data to be modified after its initial write
– Consumer tasks may receive original or updated values when they emerge from the
work queue 52

53. Swift/T: scaling of trivial foreach { } loop 100
microsecond to 10 millisecond tasks
on up to 512K integer cores of Blue Waters
53

54. Swift/T application benchmarks
on Blue Waters
54

55. Swift/T Compiler and Runtime
• STC translates high-level Swift
expressions into low-level
Turbine operations:
55
– Create/Store/Retrieve typed data
– Manage arrays
– Manage data-dependent tasks

56. x = g();
if (x > 0) {
n = f(x);
foreach i in [0:n-1] {
output(p(i));
}}
Swift code in dataflow
• Dataflow definitions create nodes in the dataflow graph
• Dataflow assignments create edges
• In typical (DAG) workflow languages, this forms a static graph
• In Swift, the graph can grow dynamically – code fragments are evaluated
(conditionally) as a result of dataflow
• In its early implementation, these fragments were just tasks
56
x = g();
x
n
foreach i … {
output(p(i));
if (x > 0) {
n = f(x); …

57. Swift/T
http://swift-lang.org

58. Swift/T optimization challenge:
distributed vars
58
http://swift-lang.org

59. Swift/T optimizations improve data
locality
59
http://swift-lang.org

60. Parallel tasks in Swift/T
• Swift expression: z = @par=32 f(x,y);
• ADLB server finds 8 available workers
– Workers receive ranks from ADLB server
– Performs comm = MPI_Comm_create_group()
• Workers perform f(x,y)communicating on comm

61. LAMMPS parallel tasks
• LAMMPS provides a
convenient C++ API
• Easily used by Swift/T
parallel tasks
foreach i in [0:20] {
t = 300+i;
sed_command = sprintf("s/_TEMPERATURE_/%i/g", t);
lammps_file_name = sprintf("input-%i.inp", t);
lammps_args = "-i " + lammps_file_name;
file lammps_input<lammps_file_name> =
sed(filter, sed_command) =>
@par=8 lammps(lammps_args);
}
Tasks with varying sizes packed into big MPI run
Black: Compute Blue: Message White: Idle

62. GeMTC: GPU-enabled Many-Task Computing
Goals:
1) MTC support 2) Programmability
3) Efficiency 4) MPMD on SIMD
5) Increase concurrency to warp level
Approach:
Design & implement GeMTC middleware:
1) Manages GPU 2) Spread host/device
3) Workflow system integration (Swift/T)
Motivation: Support for MTC on all accelerators!

63. Logging and debugging in Swift
• Traditionally, Swift programs are debugged through the log or
the TUI (text user interface)
• Logs were produced using normal methods, containing:
– Variable names and values as set with respect to thread
– Calls to Swift functions
– Calls to application code
• A restart log could be produced to restart a large Swift run
after certain fault conditions
• Methods require single Swift site: do not scale to larger runs
63

64. Logging in MPI• The Message Passing Environment (MPE)
• Common approach to logging MPI programs
• Can log MPI calls or application events – can store arbitrary data
• Can visualize log with Jumpshot
• Partial logs are stored at the site of
each process
– Written as necessary to shared
file system
• in large blocks
• in parallel
– Results are merged into a big log file
(CLOG, SLOG)
• Work has been done optimize the
file format for various queries
64

65. Swift for Really Parallel BuildsApps
app (object_file o) gcc(c_file c, string cflags[]) {
// Example:
// gcc -c -O2 -o f.o f.c
"gcc" "-c" cflags "-o" o c;
}
app (x_file x) ld(object_file o[], string ldflags[]) {
// Example:
// gcc -o f.x f1.o f2.o ...
"gcc" ldflags "-o" x o;
}
app (output_file o) run(x_file x) {
"sh" "-c" x @stdout=o;
}
app (timing_file t) extract(output_file o) {
"tail" "-1" o "|" "cut" "-f" "2" "-d" " " @stdout=t;
}
Swift code
string program_name = "programs/program1.c";
c_file c = input(program_name);
// For each
foreach O_level in [0:3] {
make file names…
// Construct compiler flags
string O_flag = sprintf("-O%i", O_level);
string cflags[] = [ "-fPIC", O_flag ];
object_file o<my_object> = gcc(c, cflags);
object_file objects[] = [ o ];
string ldflags[] = [];
// Link the program
x_file x<my_executable> = ld(objects, ldflags);
// Run the program
output_file out<my_output> = run(x);
// Extract the run time from the program output
timing_file t<my_time> = extract(out);
65

66. Abstract, extensible MapReduce in
Swiftmain {
file d[];
int N = string2int(argv("N"));
// Map phase
foreach i in [0:N-1] {
file a = find_file(i);
d[i] = map_function(a);
}
// Reduce phase
file final <"final.data"> = merge(d, 0, tasks-1);
}
(file o) merge(file d[], int start, int stop) {
if (stop-start == 1) {
// Base case: merge pair
o = merge_pair(d[start], d[stop]);
} else {
// Merge pair of recursive calls
n = stop-start;
s = n % 2;
o = merge_pair(merge(d, start, start+s),
merge(d, start+s+1, stop));
}}
66
• User needs to implement
map_function() and merg
• These may be implemented
in native code, Python, etc.
• Could add annotations
• Could add additional custom
application logic

67. Crystal Coordinate Transformation
Workflow
• Goal: Transform 3D image stack from detector coordinates to
real space coordinates
– Operate on up to 50 GB data
– Relatively light processing – I/O rates are critical
– Core numerics in C++, Qt
– Parallelism via Swift
• Challenges
– Coupling C++ to Swift via Qscript
– Data access to HDF file on distributed storage
67

68. CCTW diagrams
68

69. CCTW: Swift/T application (C++)
bag<blob> M[];
foreach i in [1:n] {
blob b1= cctw_input(“pznpt.nxs”);
blob b2[];
int outputId[];
(outputId, b2) = cctw_transform(i, b1);
foreach b, j in b2 {
int slot = outputId[j];
M[slot] += b;
}}
foreach g in M {
blob b = cctw_merge(g);
cctw_write(b);
}}
69

70. Stateful external interpreters• Desire to use high-level, 3rd party algorithms in Python, R to orchestrate
Swift workflows, e.g.:
– Python DEAP for evolutionary algorithms
– R language GA package
• Typical control pattern:
– GA minimizes the cost function
– You pass the cost function to the library and wait
• We want Swift to obtain the parameters from the library
– We launch a stateful interpreter on a thread
– The "cost function" is a dummy that returns the
parameters to Swift over IPC
– Swift passes the real cost function results back
to the library over IPC
• Achieve high productivity and high scalability
– Library is not modified – unaware of framework!
– Application logic extensions in high-level script
Load
balancingSwift
worker
Python/R
IPC GA
MPI
Process
Task
s
Resul
ts
MP
I

71. Swift/T: Makefile example: 1
app (object_file o) gcc(c_file c, string cflags[])
{
// gcc -c -O2 -o f.o f.c
"gcc" "-c" cflags "-o" o c;
}
app (x_file x) ld(object_file o[], string ldflags[])
{
// gcc -o f.x f1.o f2.o ...
"gcc" ldflags "-o" x o;
}
app (output_file o) run(x_file x)
{
"sh" "-c" x @stdout=o;
}
71
www.ci.uchicago.edu/swift
www.mcs.anl.gov/exm

72. Swift/T: Makefile example: 2
// For each
foreach O_level in [0:3]
{
// Construct file names
string my_object =
sprintf("programs/program1-O%i.o", O_level);
. . .
string O_flag = sprintf("-O%i", O_level);
string cflags[] = [ "-fPIC", O_flag ];
object_file o<my_object> = gcc(c, cflags);
object_file objects[] = [ o ];
string ldflags[] = [];
x_file x<my_executable> = ld(objects, ldflags);
72

73. Native code overview
• Turbine is a Tcl-based system
• It is easy to call to Tcl from Swift/T
• Thus, we simply need to wrap the native function in a Tcl wrapper
• You can do this manually or with SWIG
1. Build the native function as a shared library
2. Wrap the native function in Tcl with SWIG
3. Build a Tcl package
4. Call to the Tcl function from Swift
5. Run it- just make sure the package can be found at run time
73

74. Simple app
type file;
app (file out) echo_app (string s){
echo s stdout=filename(out);
}
file out <"out.txt">;
out = echo_app("Hello world!");
74

75. Foreach example
75
type file;
app (file o) simulate_app (){
simulate stdout=filename(o);}
foreach i in [1:10] {
string fname=strcat("output/sim_", i, ".out");
file f <single_file_mapper; file=fname>;
f = simulate_app();
}

76. Multiple Apps
76
type file;
app (file o) simulate_app (int time){
simulate "-t" time stdout=filename(o);
}
app (file o) stats_app (file s[]){
stats filenames(s) stdout=filename(o);
}
file sims[];
int time = 5;
int nsims = 10;
foreach i in [1:nsims] {
string fname = strcat("output/sim_",i,".out");
file simout <single_file_mapper; file=fname>;
simout = simulate_app(time); sims[i] = simout;
}
file average <"output/average.out">;
average = stats_app(sims);

77. Multi stage
77
type file;
app (file out) genseed_app (int nseeds){ genseed "-r" 2000000
"-n" nseeds stdout=@out;
}
app (file out) genbias_app (int bias_range, int nvalues){
genbias "-r" bias_range "-n" nvalues stdout=@out;
}
app (file out, file log) simulation_app (int timesteps, int
sim_range, file bias_file, int scale, int sim_count){
simulate "-t" timesteps "-r" sim_range "-B" @bias_file "-x" scale
"-n" sim_count stdout=@out stderr=@log;
}
app (file out, file log) analyze_app (file s[]){
stats filenames(s) stdout=@out stderr=@log;
}

78. Multi stage
78
# Values that shape the runint nsim = 10;
# number of simulation programs to runint steps = 1;
# number of timesteps (seconds) per simulation
int range = 100;
# range of the generated random numbersint values = 10;
# number of values generated per simulation
# Main script and data
tracef("n*** Script parameters: nsim=%i range=%i num
values=%inn", nsim, range, values);
# Dynamically generated bias for simulation ensemblefile
seedfile<"output/seed.dat">;
seedfile = genseed_app(1);
int seedval = readData(seedfile);
tracef("Generated seed=%in", seedval);
file sims[];

79. Multi stage
79
foreach i in [0:nsim-1] {
file biasfile <single_file_mapper;
file=strcat("output/bias_",i,".dat")>;
file simout <single_file_mapper;
file=strcat("output/sim_",i,".out")>;
file simlog <single_file_mapper;
file=strcat("output/sim_",i,".log")>;
biasfile = genbias_app(1000, 20);
(simout,simlog) = simulation_app(steps, range, biasfile,
1000000, values); sims[i] = simout;
}
file stats_out<"output/average.out">;
file stats_log<"output/average.log">;
(stats_out,stats_log) = analyze_app(sims);

80. 80

81. 81

82. Thank You
82