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

Threads 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

Implementation 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 Disadvantages 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 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 Disadvantages 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

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 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 User level Thread pool Kernel runs an instance of the Scheduler Scheduler Kernel runs an instance of the scheduler on each processor. P1 P2

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 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

How the kernel and scheduler work together

Hints to Kernel Add more processors This processor is idle

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.) 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.

Results

Results 2

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 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 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 95%< all RPCs are to local domain Optimize most taken path Reduce number of system boundaries that RPC crosses

Anatomy of a remote RPC SERVER CLIENT Kernel User User Protection checks Interpret and Dispatch callRPC() Message transfer Schedule Run service Protection checks Reply Message transfer Wake up thread reschedule

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 CLIENT Kernel User Protection checks callRPC() There is no need to schedule Threads here, the scheduler Can be told to just switch The two threads Copy to Stack Run service Reply Copy to Stack Resume

Multiprocessors 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

Results

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.