Fast communication and user level parallelism
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Fast Communication and User Level Parallelism. Howard Marron. Introduction. We have studied systems that have attempted to build transparent layers below the application that created properties like replication and group communication.

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We have studied systems that have attempted to build transparent layers below the application that created properties like replication and group communication.

We will look at some areas where more control has been given to the user on parallelism


  • Allows smaller granularity to programs for better parallelism and performance.

  • Will have lower overhead than processes

  • Same program will run on one machine as a multiprocessor with little or no modification

  • Threads in same process can easily communicate since they share the same address space


Do we want threads and if so where should we implement them?

Latency in μsec on a Firefly system

Advantages and problems of ult


Thread switching does not involve the kernel:

Scheduling can be application specific: choose the best algorithm.

ULTs can run on any OS. Only needs a thread library


Most system calls are blocking and the kernel blocks processes. So all threads within the process will be blocked

The kernel can only assign processes to processors. Two threads within the same process cannot run simultaneously on two processors

Advantages and problems of ULT

Advantages and inconveniences of klt


The kernel knows what the processing environment is and will assign threads accordingly.

Blocking is done on a thread level

Kernel routines can be multithreaded


Thread switching within the same process involves the kernel. We have 2 mode switches per thread switch.

This results in a significant slow down in thread switches within same process

Advantages and inconveniences of KLT

Ult with scheduler activations
ULT with Scheduler Activations

  • Implement user level threads with the help of the kernel.

  • Gain the flexibility and performance of ULT

  • Have functionality of KLT without the overhead

Ult over klt
ULT over KLT

  • Kernel operates without knowledge of user programming

  • User threads are never notified of what the kernel schedules since it is transparent to user

  • Kernel schedules threads without respect to user thread priorities and memory locations.

The model
The Model

User level

Thread pool



Kernel runs an instance of the

scheduler on each processor.



Kernel support of ult
Kernel Support of ULT

  • Kernel has control of processor allocation

  • ULT has control of what threads to run on allocated processors

  • Kernel notifies ULT scheduler of any changes to environment

  • ULT scheduler can notify Kernel of current processor needs

Scheduler activations
Scheduler Activations

  • Add processor – run a thread here

  • Processor preempted – returns state of preempted processor, can run another thread

  • Scheduler has blocked – can run thread here

  • Scheduler has unblocked – return thread to ready list

Hints to kernel
Hints to Kernel

  • Add more processors

  • This processor is idle

Critical sections
Critical Sections

  • Idea 1

    • On a CS conflict give control back to thread holding lock

    • Thread will give control back after done with CS.

    • Found that was too slow to find if thread was in CS

    • Hard to make thread give up control after CS is done

Critical sections cont
Critical Sections (Cont.)

  • Idea 2

    • Make copies of critical sections available to scheduler.

    • Compare PC of thread with CS to check if holding a lock

    • Can run the copy of CS and will return sooner than before since the release of the lock is known to the scheduler.

Threads summary
Threads Summary

  • Best solution to threads problem will lay somewhere between ULT and KLT

  • Both must cooperate for best performance

  • Want to have most of control in user level to manage threads since kernel is far away from threads

Remote procedure calls
Remote Procedure Calls

  • A technique for constructing distributed systems

  • Allows user to have no knowledge of transport system

  • Called procedure can be located anywhere

  • Strong client/server model of computing

Problems with rpc
Problems with RPC

  • Adds huge amount of overhead

    • More protection in every call

    • All calls trap to OS

    • Have to wait for response from other system

    • All calls treated the same – worst case

Ways to improve
Ways to improve

  • 95%< all RPCs are to local domain

  • Optimize most taken path

  • Reduce number of system boundaries that RPC crosses

Anatomy of a remote rpc
Anatomy of a remote RPC








Interpret and






Run service






Wake up thread


Lightweight rpc lrpc
Lightweight RPC (LRPC)

  • Create new routines for cross domain calls

  • Use RPC similar calls for cross system calls

  • Blur the line of client/server in new calls

  • Reduce number of variable copies to messages and stacks by maintaining stacks that are dedicated to individual calls

  • Eliminates needs to schedule threads on RPC receipt at server, because processor can be instructed to just switch the calling and called threads

Anatomy of a local lrpc
Anatomy of a local LRPC







There is no need to schedule

Threads here, the scheduler

Can be told to just switch

The two threads

Copy to Stack

Run service


Copy to Stack



  • Can cache whole processor contexts on idle processors

  • Instead of context switching local processor for cross domain calls, run procedure on cached processor

  • Saves on TLB misses and other exchanges like virtual memory

Lrpc conclusions
LRPC Conclusions

  • RPCs can be improved for general case

  • Common case should be emphasized not the most general case

  • Can reduce many unnecessary tasks when optimizing for cross domain tasks.