The Grid as a Parallel The Grid as a Parallel ComputerComputerFrancis C.M. Lau

Department of Computer ScienceThe University of Hong Kong

www.cs.hku.hk/~fcmlau

Greetings from Hong Kong!

Systems research @ HKU

www.srg.cs.hku.hk

hkgrid.org

HKGrid – the initial setup (2004)

www.cngrid.org

The 500th machine at 11/2005 has a peak of 2.9 Tflops

The 1st (DOE/BlueGene) has 0.37 Pflops

The 500th machine at 11/2005 has a peak of 2.9 Tflops

The 1st (DOE/BlueGene) has 0.37 Pflops

(11/2002)

Agenda

Parallel computing state of affairs

Parallel computing many faces

Grid as a parallel computer

Our first attempt – G-JavaMPI

Some thoughts for the future

The State of High Performance Computing

“Oxen vs. chickens”

• “If you were plowing a field, which would you rather use? Two strong oxen or 1024 chickens?” - Seymour Cray (’25–’96)

• Your choice?

Will time tell?

• “At best, clusters are a loose collection of unmanaged, individual, microprocessor-based computers … Most cluster [experts] know now that users are fortunate to get more than 8% of the peak performance in sustained performance.” - Dr. Paul  Terry, CTO, Cray Canada, 2004

Never to predict the future?

• “No one will need more than 640 kb of memory for a personal computer.” (Bill Gates, 1981, wrongly attributed?)

You need cpu, cpu, …

Subramanian, 1999

Software complexity

Is Grid New?

Many faces of “parallel” computing

• Distributed computing (DC)– Multiple computers remote from each other, each having a role

in a computation problem– Loose parallelism

• Cluster computing (CC)– DC on a LAN, with homogeneous processing nodes (typically

PCs), to form what appears to be a single, highly-available system

• Grid computing (GC)– A potentially very large DC operating

as an anarchy– As large as the Internet/WWW– Parallelism at stake?

• Cluster: chicken farm

• Grid: animal zoo– Enterprise grid: a private zoo in the backyard

• Distributed system: a “static” zoo where the animals are tame

From cluster to grid

• One of the main ideas of cluster computing is that, to the outside world, the cluster appears to be a single system, which is also the reason for clustering’s extreme successes

• A cluster can be programmed like a single computer, almost

• Can a grid? Should a grid?

Grid vs. service oriented computing

• To many, the two are almost synonymous– Just as Web and the Internet are almost synonymous

• SOC refers to binding to Web services at runtime– Grid is about the provisioning of resources– The current grid’s use of Web services was out of

convenience (my opinion)– But the service paradigm should

not be the only possible form ofcomputing with the grid

• You want a hamburger– you can either go toMacdonalds or do it yourself

• SOC applied to the Web (as a grid) is probably best for commercial applications (Macdonalds)

• For scientific or grand challenge problems, we need to program the grid (DIY)

The Grid as a Computer?

• Cluster: more nodes than microprocessors in each node (MPI)

• Constellation: A node has more microprocessors than # nodes (OpenMP)

• Tightly integrated MPP

• Grid?

Grid vs. clustering

• Grid: heterogeneous resources (computation, storage, networking, OS, etc.)

• Grid: dynamic (resources come and go)• Grid: distributed over a local or wide area• Grid: increased scalability (no

latency/proximity limits)• Grid: multiple ownerships• Grid and cluster are complementary

Issues

• Heterogeneity

• Availability

• Latencies

• Security and trustworthiness

• Load balancing!

• Towards single system image (SSI)

Grid: heterogeneous resources (computation, storage, networking, OS)Grid: dynamic (resources come and go)Grid: distributed over a local or wide areaGrid: increased scalability (no latency/proximity limits)Grid: multiple ownershipsGrid and cluster are complementary

Load Balancing is Key

Parallel applications

• Multiple processes, multiple threads

• Application types– SIMD (Single Instruction, Multiple Data)

• SPMD (Single program, multiple data)

– MIMD (Multiple Instruction, Multiple Data)

MIMD

Need for process/thread migration

• SIMD: Remapping (re-partitioning) of data works

• For MIMD, “processes” might grow or shrink, or come and go– Remapping of processes = process migration– Processes with large footprints (i.e., many

threads) might benefit from spreading their threads across machines

• Process migration– Initially (load distribution)– Dynamic– State capture and resume

• Thread migration– Threads are often tightly coupled and share m

uch data– Beneficial?– A big challenge

Sidetrack: Thread Migration

Thread migration works!

• Probably not suitable for grid, fine for cluster where latencies are upper-bounded

• Our experience: the JESSICA2 system– A distributed JVM– Dynamic Java thread migration– JIT compilation– Global object space– I/O redirection

JavaEnabledSingleSystemImageComputingArchitecture

JESSICA2 Architecture

Thread Migration

Global Object Space

JESSICA2JVM

A Multithreaded Java Program

JESSICA2JVM

JESSICA2JVM

JESSICA2JVM

JESSICA2JVM

JESSICA2JVM

Master Worker Worker Worker Worker Worker

JIT Compiler ModePortable Java Frame

G-JavaMPI

Towards “grid as a parallel computer”

• M-JavaMPI– “M” stands for mi

gration– For cluster

• G-JavaMPI– An outgrowth of

M-JavaMPI– “G” for grid

G-JavaMPI

Organization Organization

Identity mapping

Policy space

Warranted

Task migration (Grid traveler)

A Grid Middleware for Transparent MPI Task Migration and Runtime Scheduling

• Grid-enabled implementation of the Java language bindings of the MPI v1.1 standard

• On top of Globus Toolkit (e.g., job startup, security) and MPICH-G2 (MPI communication)

• Combining the high-level message passing interface with the Java language to support portable messaging-passing programming in a grid

• It allows you to run MPI applications written in Java across multiple machines with different architectures belonging to multiple organizations

• Classes of problems implemented in C-MPI (for example, MPICH) can be easily ported to G-JavaMPI, but with additional support of process migration

• A better choice for those people who enjoy object-oriented programming style more

• Grid-enabled implementation of the Java language bindings of the MPI v1.1 standard

• On top of Globus Toolkit (e.g., job startup, security) and MPICH-G2 (MPI communication)

• Combining the high-level message passing interface with the Java language to support portable messaging-passing programming in a grid

• It allows you to run MPI applications written in Java across multiple machines with different architectures belonging to multiple organizations

• Classes of problems implemented in C-MPI (for example, MPICH) can be easily ported to G-JavaMPI, but with additional support of process migration

• A better choice for those people who enjoy object-oriented programming style more

Special features

• Transparent dynamic process migration– Load balancing– Fault tolerance– Resource co-allocation

• Fine-grain access control through delegation– Multi-hop delagation– Cross-organization resource sharing

G-PASS

• Globus operates at the level of users, G-PASS at the level of processes

• A process can be migrated multiple times across multiple grid nodes

• The process (a “traveler”) obtains his/her privileges via a security instance (the “passport”) instead of from the hosts

• Permission to access a resource in the destination host is granted by simply checking the signature in the security instance

Instance-oriented delegation

GSI = Grid Security Infrastructure

Main components of G-JavaMPI

Runtime analysis

• Based on JVMTI (Tool Interface) – dynamically add instrument code in Java bytecode

• Identify the execution hotspots in the process

• Analyze process synchronization relationships for per-process computational requirement, and communication workload

Dynamic instrumentation

hotspothotspot

Communication performance

JMPI-BLAST cost breakdown

Ray tracing experiment

Message passing daemons

• Manage messages in queues

• Send/receive messages on behalf of processes

• Support multiple simultaneous applications

• Profiling of communication behaviors

Daemon Daemon Daemon Daemon

MPICH-G2

Gridnode

Gridnode

Gridnode

Gridnode

Messaging

Migration

• Capture process status through JVMDI (JVMTI in latest Sun J2SE 1.5)

• Recognize branching code in Java bytecode, find appropriate location to stop execution

• Recognize file operations• Instrumentation of

migrationexceptionhandlers

Process

JVMDI

File File

JVMDI

Process

Status

dump

Status

restoration

ProcessMigration

Frames and Runtime States Restoration

Migration in action

Migration-transparent message passing

• Mapping virtual process ranks to physical locations in location tables

• Processes during message passing not allowed to migrate

• Sequencing the messages, collecting legacy messages in previous node, re-sending them to new node

•N-body simulation (body shape: loop, 10000 bodies, 16 processes)•Periodical random process migrations, for demo purpose

•Ray tracing application on CNGrid•The scheduler periodically checks the workload in grid nodes and moves some processes to idle nodes•The Java applet displays the result and migration information

To find out more

• L. Chen, T.C. Ma, C.L. Wang, F.C.M. Lau, and S.P. Li, “G-JavaMPI: A Grid Middleware for Transparent MPI Task Migration”, in Engineering the Grid: Status and Perspective, American Scientific Publishers, 2006, to appear.

• T.C. Ma, C.L. Wang, L. Chen, and F.C.M. Lau, “G-PASS: An Instance-oriented Security Infrastructure for Grid Travelers”, Concurrency and Computation: Practice and Experience, to appear.

http://www.cs.hku.hk/~lchen2/G-JavaMPI/

The Future of Grid

What next?

• Grid computing today is like a “pot luck” supper– Everyone brings and

contributes a dish– And … surprise!

• There really is “no free lunch”– Everyone shares some of the

costs– Is it worth it?

POTLUCK DINNER

• To minimize the “surprises” (quality of service)– Let the pros - the chefs - do it– You sit back, relax, and enjoy, and pay for and only for

what you consume

• Grid now is a private club• But eventually it should be like …

– Ubiquitous– Invisible (the machinery behind)– It’s my “cup of coffee”

The “pervasive grid” – everyone’s club

The grid (invisible computing)

Thin clients“To use a computer

is fun, but not to manage it”

Edge computing

• Person … device … middleware (proxies) ... Internet

• The abstract cloud moves with the client – personalized “cuddleware”, nomadic computing

Internetproxies united

client

metropolis

Problems worth pursuing

• Edge computing → “seamless”– New protocols for the edge

• The continuum → the network is the computer– Collaborative models and mechanisms, esp. at the edge

• The global grid → invisible, “PC” disappearing• The device

– Adaptation• On-demand code composition

– The SOC approach?

• Content– HTML

• UI description languages

– New paradigms for user interaction in small devices (input and output)