This section give you a tutorial of how to use the Ninf-G system for programming on the Grid. Simplicity of programming is the most beneficial aspect of the Ninf-G system, and we hope that users will be able to gridify his programs easily after reading this document. We hope to extend this example further to cover more advanced Ninf-G features. Examples are provided for GRPC API.
We first cover the simple case where the library to be
Gridifyied is defined as a linkable library function.
Below is a sample code of a simple matrix multiply.
The first scalar argument specifies the size of the
matrix (n by n), parameters
a
and b
are references to matrices to be
multiplied, and c is the reference to the result matrix.
Notice that, 1) the matrix (defined as arrays) do not
itself embody size as type information, and
2) as a result there is a dependency between n and a, b, c.
In fact, since array arguments are passed as a reference,
one must assume the contents of the array are implicitly
shared by the caller and the callee, with arbitrary choices
as to using them as input, output, or temporary data structures.
void mmul(int n, double * a, double * b, double * c)
{
double t;
int i, j, k;
for (i = 0; i < n; i++) {
for (j = 0; j < n; j++) {
t = 0;
for (k = 0; k < n; k++){
t += a[i * n + k] * b[k * n + j];
}
c[i * n + j] = t;
}
}
}
The main routine which calls mmul() might be as follows:
main()
{
double A[N*N], B[N*N], C[N*N];
initMatA(N, A); /* initialize */
initMatB(N, B); /* initialize */
mmul(N, A, B, C);
}
In order to "Gridify", or more precisely, allow mmul to be called remotely via GridRPC, we must describe the interface of the function so that information not embodied in the language type system becomes sufficiently available to the GridRPC system to make the remote call. Although future standardization is definitely conceivable, currently each GridRPC system has its own IDL (Interface Description Language); for example, Ninf has its own NinfIDL definition. Below we give the interface of mmul() defined by the NinfIDL syntax:
1: Module mmul;
2:
3: Define mmul(IN int N, IN double A[N*N],
4: IN double B[N*N], OUT double C[N*N])
5: "matmul"
6: Required "mmul_lib.o"
7: Calls "C" mmul(N, A, B, C);
Line 1 declares the module name to be defined. There is a one-to-one correspondence between a module and an IDL file, and each module can have multiple entries to gridify multiple functions. Lines 3-7 are the definition for a particular entry mmul/mmul. Here, lines 3 and 4 declare the interface of the entry. The difference between a NinfIDL entry definition and the C prototype definition is that there are no return values (the return value of the Ninf call is used to return status info), argument input/output modes are specified, and array sizes are described in terms of the scalar arguments.
We note here that NinfIDL has special features to efficiently support gridifying of a library (similar features are found in Netsolve IDL). In contrast to standard procedure calls within a shared memory space, GridRPC needs to transfer data over the network. Transferring the entire contents of the array will be naturally very costly, especially for huge matrices appearing in real applications. Here, one will quickly observe that surprising number of numerical libraries take for granted the fact that address space of data structures, in particular arrays are shared, and (a) only use subarrays of the passed arrays, (b) write back results in the passed arrays, and (c) pass arrays as scratchpad temporaries. The portion of the arrays to be operated, etc., are determined by the semantics of the operation according to the input parameters passed to the function. For example in mmul, the whole arrays need to be passed, and their sizes are all N by N, where N is the first scalar parameter; A and B only need to be passed as input parameters and their contents do not change, while C is used as a return argument and thus need not be shipped to the server, but the result needs to be shipped back to the client. In general, in order to determine and minimize the size of transfer, NinfIDL allows flexible description of the array shape used by the remote library. One can specify leading dimensions, subarrays, and strides. In fact arbitrary arithmetic expressions involving constants and scalar arguments can be used in the array size expressions.
Line 5 is the comment describing the entry, while line 6 specifies the necessary object file when the executable for the particular file is to be linked. Line 7 gives the actual library function to be called, and the calling sequence; here "C" denotes C-style (row-major) array layout.
The user compiles this IDL file using the Ninf IDL compiler, and generates the stub code and its makefile. By executing this makefile a Ninf executable is generated. The user will subsequently register the executable to the server using the registry tool.
Now the client us ready to make the call of the network. In order to make a GridRPC call, the user modifies his original main program in the following manner. We notice that only the function call is modified---No need to change the program to adjust to the skeleton that the IDL generator generates as is with typical RPC systems such as CORBA. Moreover, we note that the IDL, the stub files and the executables are only resident on the server side, and the client only needs to link his program with a generic Ninf client library.
main()
{
double A[N*N], B[N*N], C[N*N];
grpc_function_handle_t handle;
grpc_initialize(argv[1]);
initMatA(N, A); /* initialize */
initMatB(N, B); /* initialize */
grpc_function_handle_default(&handle, "mmul/mmul");
if (grpc_call(&handle, N, A, B, C) != GRPC_NO_ERROR) {
fprintf(stderr, "Error in grpc_call\n");
exit(1);
}
grpc_function_handle_destruct(&handle);
...
grpc_finalize();
}
The above example assumes that the numerical routine is supplied as a library with well-defined function API, or at least its source is available in a way such that it could easily converted into a library. In practice, many numerical routines are only available in a non-library executable and/or binary form, with input/output interfaces using files. In order to gridify such "canned" applications, GridRPC systems typically support remote files and their automatic management/transfer.
We take gnuplot as an example. Gnuplot in non-interactive mode inputs script from a specified file, and outputs the resulting graph to the standard output. Below is an example gnuplot script.
set terminal postscript
set xlabel "x"
set ylabel "y"
plot f(x) = sin(x*a), a = .2, f(x), a = .4, f(x)
If this script is saved under a filename "gplot":
> gnuplot gplot > graph.ps
will store the postscript representation of the graph to the file graph.ps. In order to execute gnuplot remotely, we must package it appropriately, and moreover must automatically transfer the input (gplot) and output (graph.ps) files between the client and the server.
Ninf-G IDL provides a type filename to specify that the particular argument is a file. Below is an example of using gnuplot via GridRPC.
Module plot;
Define plot(IN filename plotfile, OUT filename psfile )
"invoke gnuplot"
{
char buffer[1000];
sprintf(buffer, "gnuplot %s > %s", plotfile, psfile);
system(buffer);
}
The IDL writes the string command sequence to invoke gnuplot into a variable buffer[], and invokes gnuplot as a system library. The file specified as an input is automatically transferred to the temporary directory of the server, and its temporary file name is passed to the stub function. As for the output file, only the temporary file name is created and passed to the stub function. After the stub program is executed, the files in output mode as specified in the IDL are automatically transferred to the client, and saved there under the name given in the argument.
Below is an example of how this function might be called via GridRPC.
#include <stdio.h>
#include "grpc.h"
main(int argc, char **argv)
{
grpc_function_handle_t handle;
grpc_initialize(argv[1]);
grpc_function_handle_default(&handle, "plot/plot");
if (grpc_call(&handle, argv[2], argv[3]) != GRPC_NO_ERROR) {
fprintf(stderr, "Error in grpc_call\n");
exit(1);
}
grpc_function_handle_destruct(&handle);
...
grpc_finalize();
}
We also note that, by combining this feature with the technique of using multiple servers simultaneously described in the next section, we can process large amount of data at once.
GridRPC can serve as a task-parallel programming abstraction, whose programs can scale from local workstations to the Grid. Here, we take an example of simple parameter sweep survey, and investigate how it can be easily programmed using GridRPC.
As an example, we compute the value of PI using a simple Monte Carlo Method. We generate a large number of random points within the square region that exactly encloses a unit circle (actually, 1/4 of a circle). We calculate the value of PI by inverse computing the area of the circle according to the probability that the points will fall within the circle. The program below shows the original sequential version.
long pi_trial(int seed, long times)
{
long l, long counter = 0;
srandom(seed);
for (l = 0; l < times; l++){
double x = (double)random() / RAND_MAX;
double y = (double)random() / RAND_MAX;
if (x * x + y * y < 1.0)
counter++;
}
return counter;
}
main(int argc, char **argv)
{
double pi;
long times = atol(argv[1]);
count = pi_trial(10, times);
pi = 4.0 * (count / (double) times);
printf("PI = %f\n", pi);
}
First, we rewrite the program so that it does the appropriate GridRPC calls. The following steps are needed:
Module pi;
Define pi_trial(IN int seed, IN long times, OUT long * count)
"monte carlo pi computation"
Required "pi_trial.o"
{
long counter;
counter = pi_trial(seed, times);
*count = counter;
}
main(int argc, char **argv)
{
double pi;
long times, count;
grpc_function_handle_t handle;
grpc_initialize(argv[1]);
times = atol(argv[2]);
grpc_function_handle_default(&handle, "pi/pi_trial");
if (grpc_call(&handle, 10, times, &count) != GRPC_NO_ERROR) {
fprintf(stderr, "Failed in grpc_call\n");
exit(2);
}
pi = 4.0 * ( count / (double) times);
printf("PI = %f\n", pi);
grpc_function_handle_destruct(&handle);
grpc_finalize();
}
We now have made the body of the computation remote. The next phase is to parallelise it.
We next rewrite the main program so that parallel tasks are distributed to multiple servers. Although distribution of tasks are possible using metaserver scheduling with Ninf (and Agents with Netsolve), it is sometimes better to specify a host explicitly for performance reasons, for low overhead and explicit load balancing. Ninf-G allows explicit specification of servers by specifying the hostname in the initialization of the function handle.
The standard grpc_call() RPC is synchronous in that the client waits until the completion of the computation on the server side. For task-parallel execution, Ninf-G facilitates several asynchronous call APIs. For example, the most basic asynchronous call grpc_call_async is almost identical to grpc_call except that it returns immediately after all the arguments have been sent. The return value is the session ID of the asynchronous call; the ID is used for various synchronizations such as waiting for the return value of the call.
There are several calls for synchronization.
The most basic is the
grpc_wait(grpc_sessionid_t ID)
,
where we wait for the result of the asynchronous call
with the supplied session ID.
grpc_wait_all() waits for all preceding asynchronous
invocations made.
Here, we employ grpc_wait_all() to parallelize
the above PI client so that it uses multiple
simultaneous remote server calls:
1 #include "grpc.h"
2 #define NUM_HOSTS 8
3 char * hosts[] = {"node0.example.org", "node1.example.org", "node2.example.org", "node3.example.org",
4 "node4.example.org", "node5.example.org", "node6.example.org", "node7.example.org"};
5
6 grpc_function_handle_t handles[NUM_HOSTS];
7 grpc_sessionid_t ids[NUM_HOSTS];
8
9 main(int argc, char **argv){
10 double pi;
11 long times, count[NUM_HOSTS], sum;
12 char * config_file;
13 int i;
14 if (argc < 3) {
15 fprintf(stderr, "USAGE: %s CONFIG_FILE TIMES \n", argv[0]);
16 exit(2);
17 }
18 config_file = argv[1];
19 times = atol(argv[2]) / NUM_HOSTS;
20
21 /* Initialize GRPC runtimes. */
22 if (grpc_initialize(config_file) != GRPC_NO_ERROR) {
23 exit(2);
24 }
25 /* Initialize handles. */
26 for (i = 0; i < NUM_HOSTS; i++)
27 grpc_function_handle_init(&handles[i], hosts[i], "pi/pi_trial");
28
29 for (i = 0; i < NUM_HOSTS; i++) {
30 /* Parallel non-blocking remote function invocation. */
31 if (grpc_call_async(&handles[i], &ids[i], i, times, &count[i]) != GRPC_NO_ERROR){
32 grpc_perror_np("pi_trial");
33 exit(2);
34 }
35 }
36 /* Sync. */
37 if (grpc_wait_all() != GRPC_NO_ERROR) {
38 grpc_perror_np("wait_all");
39 exit(2);
40 }
41
42 for (i = 0; i < NUM_HOSTS; i++)
43 grpc_function_handle_destruct(&handles[i]);
44
45 /* Compute and display pi. */
46 for (i = 0, sum = 0; i < NUM_HOSTS; i++)
47 sum += count[i];
48 pi = 4.0 * ( sum / ((double) times * NUM_HOSTS));
49 printf("PI = %f\n", pi);
50
51 /* Finalize GRPC runtimes. */
52 grpc_finalize();
53 }
We specify the number of server hosts and their names in lines 2 and 3-4, respectively. Line 6 is the port number used, and line 19 divides the number of trials with the number of servers, determining the number of trials per server. The for loop in lines 29-35 invokes the servers asynchronously. Line 47 aggregates the results returned from all the servers.
In this manner, we can easily write a parallel parameter sweep survey program using the task parallel primitives of GridRPC. We next modify the program to perform dynamic load balancing.


Implementations for accessing IngestInformation entities on server-side directly. This includes the IngestInformationPersistenceImpl for direct access to the JPA persistence backend and the IngestInformationServiceLocal implementing a common service interface. Typically, the IngestInformationServiceLocal should be used.


Created by:
GitHub Username/URL
Level 2 test for : JavaScript
ping pong count up to your number from 1 with the following exceptions:
Created by; Donnah Adhiambo


--- title: Feather – Simply beautiful open source icons description: Designed on a 24x24 grid with an emphasis on functionality, consistency and simplicity. links: - title: Donate url: https://www.paypal.com/cgi-bin/webscr?cmd=_donations&business=G6CPFZ6PQRZW8&lc=US&item_name=Feather¤cy_code=USD&bn=PP%2dDonationsBF%3abtn_donateCC_LG%2egif%3aNonHosted - title: GitHub url: https://github.com/colebemis/feather - title: Change Log url: https://github.com/colebemis/feather/blob/gh-pages/CHANGELOG.md - title: License url: https://github.com/colebemis/feather/blob/gh-pages/LICENSE - title: Contact url<EMAIL_ADDRESS>---


The informations needed for cluster creation
- Name
- An arbitrary human-readable name.
- Profile
- Profile used for this cluster.
- Min Size
- Min size of the cluster. Default to 0.
- Max Size
- Max size of the cluster. Default to -1 means unlimited.
- Desired Capacity
- Desired capacity of the cluster. Default to 0.
- Timeout
- Cluster creation timeout in seconds.
- Metadata
- The metadata yaml used to create the profile.


|
Your job is to bring water back home to a family before it evaporates and you lose everything!
Use the ↑ arrow key to jump over any obstacles, Use the ↓ arrow key to duck under any aerial obstacles.
Be careful - you will lose water if you bump into obstacles and lose water over time!
|


paint
or
update
method is automatically invoked with the appropriate
Graphics
context.
The Java 2D API includes the
java.awt.Graphics2D
class, which extends the
Graphics
class to provide access to the enhanced graphics and rendering
features of the Java 2D API. These features include:
draw
method).
fill
method).
drawString
method). The font
attribute is used to convert the string to glyphs, which are then
filled with the color or pattern specified by the paint attributes.
drawImage
method).
In addition, the Graphics2D
class supports the Graphics
rendering methods for particular shapes, such as drawOval
and fillRect
. All methods that are represented above can be divided into two groups:
Graphics2D
context for following purposes:
To employ Java 2D API features in the application, cast the Graphics
object
passed into a component’s rendering method to a Graphics2D
object. For example:
public void paint (Graphics g) { Graphics2D g2 = (Graphics2D) g; ... }
As the following figure shows, the Graphics2D
class rendering context contains several attributes.
| The pen attribute is applied to the outline of a shape. This stroke attribute enables you to draw lines with any point size and dashing pattern and apply end-cap and join decorations to a line. | |
| The fill attribute is applied to a shape's interior. This paint attribute enables you to fill shapes with solid colors, gradients, and patterns. | |
| The compositing attribute is used when rendered objects overlap existing objects. | |
| The transform attribute is applied during rendering to convert the rendered object from user space to device-space coordinates. Optional translation, rotation, scaling, or shearing transforms can also be applied through this attribute. | |
The clip,
type restricts rendering to the area
within the outline of the Shape
object used to define the clipping path. Any Shape
object that is used to define the clip.
|
|
|
The font attribute is used to convert text strings to glyphs. |
|
| Rendering hints specify preferences in the trade-offs between speed and quality. For example, you can specify whether antialiasing should be used, if this feature available. See also Controlling Rendering Quality. |
To learn more about transforming and compositing see the Advanced Topics in Java2D lesson.
When an attribute is set, the appropriate attribute object is passed.
As the following example shows, to change the paint attribute to a blue-green
gradient fill, you construct a GradientPaint
object
and then call the setPaint
method.
gp = new GradientPaint(0f,0f,blue,0f,30f,green); g2.setPaint(gp);


Axel Engblom
Front End Developer
About Me
26 years old
I live in Axvall, Sweden
I am a Front End Student
I play bowling in my free time
Cat
My Skills
HTML5
CSS3
Javascript
Macbook Pro
My Projects
Coming Soon


Overview
Tic Tac Toe
Sign Up
Next
Username already taken
Have an account?
Log In
Profile Picture
Set
Finish
Capture
Capture
Upload
Cancel
© Veličković Đorđe


Neural Magic Documentation¶
Neural Magic’s Deep Sparse Platform provides a suite of software components to select, build, and run performant deep learning models on CPU resources.
To learn more about specific projects founded by Neural Magic, check out documentation links below.
To file a docs issue for a specific software component, use its repo issue tracker.
Docs |
GitHub |
Issue Tracker |
Releases |
License |
|---|---|---|---|---|
Apache 2.0 |
||||
Apache 2.0 |
||||
Apache 2.0 |
||||
Neural Magic Engine |
To contribute to Neural Magic documentation, please:
Open a GitHub issue and give it the documentation label.
Submit a pull request with your proposed documentation changes.
Get started with writing and formatting on GitHub
To learn more about Neural Magic, check our website.


Welcome to Larry’s hints for using Python¶
This site contains snippets demonstrating how to do things I had trouble doing in hopes of making them easier to find when I need to do them again.
This site contains snippets demonstrating how to do things I had trouble doing in hopes of making them easier to find when I need to do them again.


Helped IBC & AHA reduce MIPS footprint and migrate off Mainframe systems
Zero post release defects reported by the client
61% EBITDA gained through fixed bid mode of execution
Created a new skill set pool of MicroFocus resources
Case study for UST COO’s Office to create Narrative for Enterprise Modernization
Reference project in new pursuit for Modernization project for Viking Cruises
95.5% of the regression test scripts at aha and front office automated
1500+ Test Scripts from 25 applications automated
Additional 1600+ scripts in pipeline for phase 3
Estimated Annual Cost Savings of $602,470 for IBC and AHA from Phase 1 & 2
Executed in fixed bid mode with EBITDA of 51%
Helped IBC in finalizing UI Path as the RPA tool
Automated 5 business processes for IBC leveraging RPA
Conducted RPA March (idea drive) which generated 45 ideas which are now added to pipeline of RPA efforts at IBC
Shift in Digital Services – Grew to 46% in 2018 from 30% in 2017
Bots to assist test data entry in claims adjudication system and test data management
AI/ ML, RPA based solutions proposed to augment Medical Chart Review services for Risk Adjustment; initial POC demoed
Our clients stood reference for 4 new potential clients – Experian, BCBSA, BCBS AZ, Viking Cruises +G11+G8
Largest Wallet Share at IBC amongst IT Vendors
100% SLA Compliance
Supporting 66 initiatives at IBC and AHA
Increased Chart Review Services footprint from $0.1M in 2017 to $0.5M in 2018
$0.5M new BPO Services Revenue for AHA Claims Adjudication
3 new projects executed as Fixed Bid with a TCV of $1.2M
Won Provider Credentialing RFP against Highmark and Accenture
$1.7M TCV signed in Oct 2018 alone leveraging client’s 2018 unused funds
UST to be engaged in 3 new areas for Business Operations
5 Sr Director and Directors from IBC visited UST Campus further strengthening our relationship
Our clients stood reference for 4 new potential clients – Experian, BCBSA, BCBS AZ, Viking Cruises
UpSkilling Automation to Manual Tester Ratio
Dec 2017- 38.62
Oct 2018- 76.24
UpSkilling Cross Training MuleSoft – 6 Bizagi – 20 Uipath – 10 Liferay – 3 Biz Talk – 5 Selenium/UFT – 19
AGILE AND CI/CD READINESS 80% of the team have undergone Agile and/or VSTS training 8 certified scrum masters
Our clients stood reference for 4 new potential clients – Experian, BCBSA, BCBS AZ, Viking Cruises
Covers the cost of medical care, started in 1920s
Payer - provides a health insurance
Member - avail a health insurance from the payer
Providers - provide medical/clinical services
Deductible - amount to pay before the insurance starts to pay
Copay - fee paid each time you see a doctor or fill a prescription
Coinsurance - money to pay after you have paid deductible
Indemnity - health plan that do not use deductible or copay
Managed Care - delivering comprehensive care services
CDHP - Consumer Directed Health Plan
FSA - oldest type of CDHP account
Administered by CMS
For people aged 65 or above and people with disability
For people with low income rate who cannot afford healthcare
Main Group accounts for Medicaid is mainly for Elderly and disabled adults
Medicare Part A -Inpatient coverage -Covers hospital services
Medicare Part B -Outpatient coverage -Covers Physician’s professional services
Medicare Part C -Medicare Advantage -Provides Part A, Part B and sometimes even Part D coverage
Medicare Part D -Drug Coverage -Introduced in 2006-Covers the prescribed drugs
Covers military personel, military retirees
Managed by the Defense Health Agency (DHA)
An individual plan is taken by one person
A family plan is for two or more members
Small group plans premium must on benefits and quality improvement
Large group plans premium dollars collected by insurance companies
Premium depends on prior claim history


KST Awarded GSA STARS II Four Year Contract for IT Services
Ka’ala Systems Technology Corp. Awarded GSA STARS II contract June 2017 – August 2021 for IT Services.
Contract# GS00Q17GWD2199
Ka’ala Systems Technology (KST) Corp. was awarded the Prime contractor for 8(a) STARS II for Governmentwide Acquisition Contracts (GWACs). It is a competitively awarded, multiple-award, indefinite-delivery, indefinite-quantity contract for requirements associated with four North American Industrial Classification System (NAICS) for Information Systems Technology services.
STARS II enables federal agencies to fulfill mission requirements and, at the same time, assists in meeting or exceeding socioeconomic goals through the utilization of businesses which were certified 8(a) eligible by the Small Business Administration (SBA). Federal agencies may earn 8(a) credit and other applicable socioeconomic procurement preference credits1 that each industry partner possesses. These socioeconomic designations are indicated on the STARS II industry partner list on the STARS II website.
STARS II supports direct award task orders under the $4 Million competitive threshold on a sole-source basis. When requirements exceed the $4 Million contract holders must compete.
The 8(a) STARS II functional areas correspond to four North American Industry Codes (NAICS). For each FA, the work examples in the bulleted list under each NAICS code description are representative, not all inclusive.
| NAICS 541511 | Custom Computer Programming Services |
| NAICS 541512 | Computer Systems Design Services |
| NAICS 541513 | Computer Facilities Management Services |
| NAICS 541519 | Other Computer Related Services |
Functional Area 1 – NAICS 541511 – Custom Computer Programming Services
This U.S. industry comprises establishments primarily engaged in writing, modifying, testing, and supporting software to meet the needs of a particular customer.
- Custom computer applications software programming services;
- Custom computer program or software development;
- Custom computer programming services;
- Custom computer software analysis and design services;
- Custom computer software programming services;
- Custom computer software support services; and
- Web (i.e. Internet) page design services, custom.
Functional Area 2 – NAICS 541512 – Computer Systems Design Services
This U.S. industry comprises establishments primarily engaged in planning and designing computer systems that integrate computer hardware, software, and communication technologies. The hardware and software components of the system may be provided by this establishment or company as part of integrated services, or may be provided by third parties or vendors. These establishments often install the system and train and support users of the system.
- CAD (computer-aided design) systems integration design services;
- CAE (computer-aided engineering) systems integration design;
- CAM (computer-aided manufacturing) systems integration design;
- Computer hardware consulting services or consultants;
- Computer software consulting services or consultants;
- Computer systems integration analysis and design services;
- Computer systems integration design consulting services;
- Computer integrator services;
- Information management computer systems integration design services;
- Local Area Network (LAN) computer systems integration design services;
- Computer network systems design services; and
- Office automation computer systems integration design services.
Functional Area 3 – NAICS 541513 – Computer Facilities Management Services
This U.S. industry comprises establishments primarily engaged in providing on-site management, and operation of client’s computer systems and/or data processing facilities. Establishments providing computer systems or data processing facilities support services are included in this industry.
- Computer systems facilities management and operation services;
- Data processing facilities management and operation services;
- Computer systems or data processing facilities management and operation services; and
- Computer systems or data process facilities support services.
Functional Area 4 – NAICS 541519 – Other Computer Related Services
This U.S. industry comprises establishments primarily engaged in providing computer-related services (except custom programming, systems integration design, and facilities management services). Establishments providing computer disaster recovery services or software installation services are included in this industry.
- Computer disaster recovery services; and
- Computer software installation services.