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THERE ARE NOT ENOUGH SLOTS AVAILABLE IN THE SYSTEM TO SATISFY MPI

* There Are Not Enough Slots Available In The System To Satisfy Mpi Online
* There Are Not Enough Slots Available In The System To Satisfy Mpi Free
* Mpiexec There Are Not Enough Slots Available In The System To Satisfy

In computer science, gang scheduling is a scheduling algorithm for parallel
systems that schedules related threads or processes to run simultaneously on
different processors. Usually these will be threads all belonging to the same
process, but they may also be from different processes, where the processes
could have a producer-consumer relationship or come from the same MPI program.

GPU boards are wide enough to cover two physically adjacent PCI-e slots, so make
sure that the PCIe x16 and x8 slots are physically separated on the motherboard
so that you can fit a minimum of 2 PCI-e x16 GPUs and 1 PCIe x8 network card.
Choose the right form factor for GPUs.

Gang scheduling is used to ensure that if two or more threads or processes
communicate with each other, they will all be ready to communicate at the same
time. If they were not gang-scheduled, then one could wait to send or receive a
message to another while it is sleeping, and vice versa. When processors are
over-subscribed and gang scheduling is not used within a group of processes or
threads which communicate with each other, each communication event could suffer
the overhead of a context switch.

* $ mpirun -host hostgui,hostser -np 10 mpi3 - There are not enough slots
available in the system to satisfy the 10 slots that were requested by the
application: mpi3 Either request fewer slots for your application, or make
more slots available for use.
* R3.4 + OpenMPI 3.0.0 + Rmpi inside macOS – little bit of mess;) As usual,
there are no easy solutions when it comes to R and mac;) First of all, I
suggest to get clean, isolated copy of OpenMPI so you can be sure that your
installation has no issues with mixed libs.
* 版本: Open MPI 3.0.1 编译好可执行的C语言程序后，使用 mpirun -np 3 Test 命令，发现没有正常运行，而是报错： There
are not enough slots available in the system to satisfy the 3 slotsthat were
requested by the application: /home/.
* There are not enough slots available in the system to satisfy the 6 slots
that were requested by the application: fdsmpi Either request fewer slots for
your application, or make more slots available for use.-totoro@TOTORO:/ FDS /
FDS6 / Examples / ThreadCheck / test1$.

Gang scheduling is based on a data structure called the Ousterhout matrix. In
this matrix each row represents a time slice, and each column a processor. The
threads or processes of each job are packed into a single row of the matrix.[1]
During execution, coordinated context switching is performed across all nodes to
switch from the processes in one row to those in the next row.

Gang scheduling is stricter than coscheduling.[2] It requires all threads of the
same process to run concurrently, while coscheduling allows for fragments, which
are sets of threads that do not run concurrently with the rest of the gang.

Gang scheduling was implemented and used in production mode on several parallel
machines, most notably the Connection Machine CM-5.

TYPES[EDIT]

BAG OF GANGS (BOG)[EDIT]

In gang scheduling, one to one mapping happens, which means each task will be
mapped to a processor. Usually, jobs are considered as independent gangs, but
with a bag of gangs scheme, all the gangs can be combined and sent together to
the system. When jobs are executed in the system, the execution can never be
completed until and unless all the gangs that belong to the same BoG complete
their executions.[3] Thus, if one gang belonging to some job completes its
execution, it will have to wait until all the gangs complete their executions.
This leads to increased synchronization delay overhead.

Response time Rj{displaystyle R_{j}} of jth{displaystyle j^{th}} Bag of Gangs is
defined as the time interval from the arrival of the BoG at the grid dispatcher
to the completion of jobs of all of the sub-gangs which belong to the BoG. The
average response time is defined as follows:

Response Time (RT)=1N∑j−1NRj{displaystyle {frac {1}{N}}textstyle sum
_{j-1}^{N}R_{j}displaystyle }.[3]

The response time is further affected when a priority job arrives. Whenever a
priority job arrives at the system, that job will be given priority with respect
to all other jobs, even over the ones which are currently being executed on the
processors. In this case, when a priority job arrives, the sub-gang which is
currently executing on the system will be stopped and all the progress that has
been made will be lost and need to be redone. This interruption of the job will
further delay the total response time of the BoG.[3]

ADAPTED FIRST COME FIRST SERVED (AFCFS)[EDIT]

Adapted first come first served (AFCFS) scheme is the adapted version of first
come and first serve scheme. As per the first-come, first-served scheme,
whichever job that comes first will be forwarded for execution. But in the AFCFS
scheme, once a job arrives at the system, the job will not be scheduled unless
and until enough processors are available for the execution of the respective
job.[3] When a large job arrives at the system and is present at the start of
the ready queue but not enough processors are available, then an AFCFS policy
will schedule the smaller job for which enough processors are available, even if
that job is at the back of the queue. In other words, this scheme favors smaller
jobs as compared to larger jobs based on the availability of processor, thus
this will leads to increased fragmentation in the system.[3][4]

LARGEST GANG FIRST SERVED (LGFS)[EDIT]

In the above execution scheme, the tasks which correspond to increasing job size
are placed in a queue, with the tasks belonging to the largest gang scheduled
first, but this method of execution tends to lead to the starvation of resources
of smaller jobs and is therefore unfit to be executed in systems where the
number of processors is comparatively low.[5]

The AFCFS and LGFS also have to deal with possible processor failure. In such a
case, tasks executing on that processor are submitted to other processors for
execution. The tasks wait in the head of the queue on these processors while the
current processor is being repaired.

There are two scenarios which emerge from the above issue:[5]

* Blocking case: The processors assigned to the interrupted jobs are blocked
and cannot execute other jobs in their queue until the jobs from the damaged
processors are cleared.[5]
* Non-blocking case: This case is incurred when the jobs already executing in
the processors are processed early instead of waiting for the blocked jobs to
resume execution.[5]

PAIRED GANG SCHEDULING[EDIT]

Gang scheduling while executing the I/O bound processes keeps the CPUs idle
while awaiting responses from the other processors, whereas the idle processors
can be utilized for executing tasks. If the gang consists of a mix of CPU and
I/O Processes, since these processes interfere little in each other’s operation,
algorithms can be defined to keep both the CPU and the I/O busy at the same time
and exploiting parallelism. This method would present the idea of matching pairs
of gangs, one I/O based and one CPU bound. Each gang would assume that it is
working in isolation as they utilize different devices.[6]

SCHEDULING ALGORITHM[EDIT]

* General case: In the general case, a central node is designated in the
network to handle task allocation and the resource allocation. It maintains
the information in an Ousterhout matrix. In strict gang scheduling, one row
is selected at a time following which a node scheduler schedules a process in
the respective cell of that row.[6]
* Paired gang: In paired gang scheduling, two rows are selected instead of one,
one each of the I/O bound gang and CPU gang. It is at the discretion of the
local scheduler to allot jobs to the appropriate processors in order to
elicit maximum allowed parallelism.[6]

SYNCHRONIZATION METHODS[EDIT]

CONCURRENT GANG SCHEDULING (CGS)[EDIT]

Concurrent gang scheduling a highly scalable and versatile algorithm and assumes
the existence of a synchronizer utilizing the internal clock of each node. CGS
primarily consists of the following three components.[7]

* Processor/Memory module (Also called Processing Element).
* 2-way network which allows 1-1 Communication.
* A synchronizer which performs synchronization of all PE’s after a constant
interval.

The synchronization algorithm is performed in two stages.[7]

* When the load changes, a dedicated time table is created by the front end
scheduler.
* Local scheduler then follows the time table by switching between the jobs
that have been distributed to them by the front end scheduler.

We assume the existence of a synchronizer that sends the signal to all the nodes
in a cluster at a constant interval. The CGS utilizes the fact that the most
common events which occur in a PC are timer interrupts and they use the same
parameter to be the internal clock.[7]

* A common counter is initialized which gets incremented every time an
interrupt is encountered and is designated the OS's internal clock.
* All nodes are synchronized after a checking interval 't' and utilize the
internal clocks of the individual nodes.
* If after time t there is no discrepancy of the individual clock of the nodes
and the global clock, time interval t is extended. Otherwise it is shortened.
* Constantly check and update checking interval t.

SHARE SCHEDULING SYSTEM[EDIT]

The SHARE scheduling system utilizes the internal clock system of each node and
is synchronized using the NTP Protocol. The flavor of scheduling is implemented
by collecting jobs with same resource requirements in a group and executing the
same for a pre-defined time-slice. Incomplete jobs are pre-empted after the time
slice is exhausted.[8]

The local memory of the node is utilized as the swap space for pre-empted jobs.
The main advantages of the SHARE scheduled system are that it guarantees the
service time for accepted jobs and supports both batch and interactive jobs.

Synchronization:

Each gang of processes utilizing the same resources are mapped to a different
processor. The SHARE system primarily consists of three collaborating
modules.[8]

* A global scheduler: This scheduler directs the local scheduler the specific
order in which to execute their processes (local gang members).
* A local scheduler: After the local scheduler is provided the jobs to execute
by the global scheduler, it ensures that only one of the parallel process is
executed at any one of the processors in a given time slot. The local
scheduler requires a context switch to preempt a job once its time slice has
expired and swap a new one in its place.
* Interface to the communication system: The communication subsystem must
satisfy several requirements which greatly increase the overhead requirements
of the scheduler. They primarily consist of:
* Efficiency: Must expose hardware performance of the interconnect to the
client level.
* Access Control: Must manage access to the communication subsystem
* Protection and Security: The interconnect must maintain separation of the
processors by not allowing one to affect the performance of another.
* Multi-Protocol: the interconnect must be able to map various protocols
simultaneously to cater to different client needs.

THERE ARE NOT ENOUGH SLOTS AVAILABLE IN THE SYSTEM TO SATISFY MPI ONLINE

PACKING CRITERIA[EDIT]

A new slot is created when we cannot pack the job into the available slot. In
case, a new slot is opened even if the job can be packed in the available slot,
then the run fraction which is equal to one over the number of slots used will
increase. Therefore, certain algorithms have been devised on packing criteria
and are mentioned below:

CAPACITY BASED ALGORITHM[EDIT]

This algorithm monitors the slots capacity and decides whether there is any need
of opening a new slot. There are two variants on this algorithm:

1. First fit. Here, the used slots are checked for capacity in a sequential
order then the first one which is having sufficient capacity is chosen. If none
of the available slots have enough capacity, a new slot is opened and the
processing elements (PE) are allocated in the slot in sequential order.[9]

2. Best fit. Unlike first fit, the used slots are sorted based on capacity, but
not in sequential order. The slot with the smallest sufficient capacity is
chosen. If none of the used slots have sufficient capacity, then only one new
slot is opened. Once the new slot is opened, the processing elements (PEs) are
allocated in the slot in sequential order as per the previous algorithm.[9]

LEFT-RIGHT BASED ALGORITHMS[EDIT]

This algorithm is a modified version of the best fit algorithm. In the best fit
algorithm, the PEs are allocated in a sequential order, but in this algorithm,
the PEs can be inserted from both directions so as to reduce the overlap between
different sets of PEs assigned to different jobs.[9]

1. Left-right by size. Here, the PEs can be inserted in sequential order and in
reverse sequential order based on the size of the job. If the size of the job is
small, the PEs are inserted from left to right and if the job is large, the PEs
are inserted from right to left.[9]

2. Left-right by slots. Unlike the previous algorithm, where the choice was
based on the size of the job, here the choice is dependent on the slot. Now,
slots are indicated as being filled, i.e. being filled from the left or from the
right. The PEs are allocated to the job in the same order. The number of slots
on both sides is approximately equal, so when a new slot is opened, the
direction is indicated based on the number of slots in both direction.[9]

LOAD BASED ALGORITHMS[EDIT]

Both the capacity-based and left-right based algorithms do not accommodate the
load on individual PEs. Load-based algorithms take into account the load on the
individual PE while tracking the overlap between sets of PEs assigned to
different jobs.[9]

1. Minimal maximum load. In this scheme, PEs are sorted based on the load on
them that each job will have on the PEs. The availability of the free PEs in the
slot determines the capacity of the slot. Suppose that PEs are allocated to a
job which has x{displaystyle x} threads, the xth{displaystyle x^{th}}PE in the
load order (last one) will determine the maximum load that any PE can have which
is available in the slot. The slot which has minimal maximum load on any PE is
chosen and a number of least loaded free PEs are used in the slot.[9]

2. Minimal average load. Unlike the previous scheme, in which slots were chosen
based on the minimal maximum load on xth{displaystyle x^{th}} PE, here slots are
chosen based on the average of the load on the x{displaystyle x} least loaded
PEs.[9]

BUDDY BASED ALGORITHM[EDIT]

In this algorithm the PEs are assigned in clusters, not individually. The PEs
are first partitioned into groups that are power of two. Each member of the
group will be assigned a controller and when a job of size n arrives, it is
assigned to a controller of size 2[lg 2] (the smallest power to 2 that is larger
than or equal to n). The controller is assigned by first sorting all the used
slots, and then identifying groups of 2[lg 2] contiguous free processors. If a
controller has all the PEs free in some of the slots, then only a newly arrived
job will be assigned to that controller. Otherwise a new slot is opened.[9]

MIGRATION BASED ALGORITHM[EDIT]

In all the above-mentioned algorithms, the initial placement policy is fixed and
jobs are allocated to the PEs based on that. However, this scheme migrates jobs
from one set of PEs to another set of PEs, which in turn improves the run
fraction of the system. [9]

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