Optimizing your Java Applications for multi-core hardware Prashanth K Nageshappa prashanth.k.n@in.ibm.com Java Technologies IBM

Agenda Evolution of Processor Architecture Why should I care? Think about Scalability How to exploit Parallelism in Java JVM optimizations for multi-core scalability

As The World Gets Smarter, Demands On IT Will Grow Smart supply chains Intelligent oil field technologies Smart food systems Smart healthcare Smart energy grids Smart retail 10x 1 Trillion 25 Billion Devices will be connected to the internet by 2011 Global trading systems are under extreme stress, handling billions of market data messages each day Digital data is projected to grow tenfold from 2007 to 2011. 70% on average is spent on maintaining current IT infrastructure versus adding new capabilities IT infrastructure must grow to meet these demands global scope, processing scale, efficiency

Hardware Trends Increasing transistor density Clock Speed leveling off More number of cores Non-Uniform Memory Access Main memory getting larger

In 2010 POWER Systems Brings Massive Parallelism 4 threads/core 8 cores/chip 32 sockets/server 1024 threads Threads POWER6™ 2 threads/core 2 cores/chip 32 sockets/server 128 threads POWER5™ 2 threads/core 2 cores/chip 32 sockets/server 128 threads POWER4™ 1 thread/core 2 cores/chip 16 sockets/server 32 threads 2001 180 nm 2004 130 nm 2007 65 nm 2010 45 nm

Agenda Evolution of Processor Architecture Why should I care? Think about Scalability How to exploit Parallelism in Java JVM optimizations for multi-core scalability

Your application may be re-used Better performance Why should I care? Your application may be re-used Better performance Better leverage additional resources Cores, hardware threads, memory etc

Think about scalability Serial bottlenecks inhibit scalability Organize your application into parallel tasks Consider TaskExecutor API Too many threads can be just as bad as too few Do not rely on JVM to discover opportunities No automatic parallelization Java class libraries do not exploit vector processor capabilities

Think about scalability Load imbalance Workload not evenly distributed Consider breaking large tasks into smaller ones Change serial algorithms to parallel ones Tracing and I/O Bottleneck unless infrequent updates or log is striped (RAID) Blocking disk/console I/O inhibit scalability

Synchronization and locking J9's Three-tiered locking Spin Yield OS Avoid synchronization in static methods Consider breaking long synchronized blocks into several smaller ones May be bad if results in many context switches Java Lock Monitor (JLM) tool can help http://perfinsp.sourceforge.net/jlm.html

Synchronization and locking Volatiles Compiler will not cache the value Creates memory barrier Avoid synchronized container classes Building scalable data structures is difficult Use java.util.concurrent (j/u/c) Non-blocking object access Possible with j/u/c

Agenda Evolution of Processor Architecture Why should I care? Think about Scalability How to exploit Parallelism in Java JVM optimizations for multi-core scalability

java.util.concurrent package Introduced in Java SE 5 Alternative strong synchronization Lighter weight, better scalability Comparing to intrinsic locks java.util.concurrent.atomic.* java.util.concurrent.locks.* ConcurrentCollections Synchronizers TaskExecutor

j/u/c/atomic.* Atomic primitives Strong form of synchronization But does not use lock – non blocking Exploit atomic instructions such as compare- and-swap in hardware Supports compounded actions AtomicBoolean AtomicInteger AtomicIntegerArray AtomicIntegerFieldUpdater AtomicLong AtomicLongArray AtomicLongFieldUpdater AtomicMarkableReference AtomicReference AtomicReferenceArray AtomicReferenceFieldUpdater AtomicStampedReference

j/u/c/atomic.* Getter and setters Updates CAS Conversions get set lazySet Updates getAndSet getAndAdd/getAndIncrement/getAndDecrement addAndGet/incrementAndGet/decrementAndGet CAS compareAndSet/weakCompareAndSet Conversions toString, intValue, longValue, floatValue, doubleValue

j/u/c/locks.* Problems with intrinsic locks j/u/c/locks Impossible to back off from a lock attempt Deadlock Lack of features Read vs write Fairness policies Block-structured Must lock and release in the same method j/u/c/locks Greater flexibility for locks and conditions Non-block-structured Provides reader-writer locks Why block other readers? Better scalability

j/u/c/locks.* Interfaces: Classes: Condition Lock ReadWriteLock ReentrantLock ReentrantReadWriteLock LockSupport AbstractQueuedSynchronizer

j/u/c.* - Concurrent Collections Concurrent, thread safe implementations of several collections HashMap → ConcurrentHashMap TreeMap → ConcurrentSkipListMap ArrayList → CopyOnWriteArrayList ArraySet → CopyOnWriteArraySet Queues → ConcurrentLinkedQueue or one of the blocking queues

Strains on the VM Excessive use of temporary memory can lead to increased garbage collector activity Stop the world GC pauses the application Excessive class loading Updating class hierarchy Invalidating JIT optimizations Consider creating a “startup” phase Transitions between Java and native code VM access lock

Memory Footprint Little control over object allocation in Java Small short lived objects are easier to cache Large long lived objects likely to cause cache misses Memory Analysis Tool (MAT) can help Consider using large pages for TLB misses -Xlp, requires OS support Tune your heap settings Heap lock contention with flat heap

Affinitizing JVMs Can exploit cache hierarchy on a subset of cores JVM working set can fit within the physical memory of a single node in a NUMA system Linux: taskset, numactl Windows: start

Is my application scalable? Low CPU means resources are not maximized Evaluate if application has too few/many threads Locks and synchronization Network connections, I/O Thrashing working set is too large for physical memory High CPU is generally good, as long as resources are spent in application threads, doing meaningful work Evaluate where time is being spent Garbage collection VM/JIT OS Kernel functions Other processes Tune, tune, tune

Write Once, Tune Everywhere HealthCenter, GCMV, MAT http://www.ibm.com/developerworks/java/jdk/tools/ Dependence on operating System Memory allocation Socket layer Tune for hardware capabilities How many cores? How much memory? What is the limit on network access? Are there storage bottlenecks?

Agenda Evolution of Processor Architecture Why should I care? Think about Scalability How to exploit Parallelism in Java JVM optimizations for multi-core scalability

IBM Java Execution Model is Built for Parallelism Generates high performance code for application threads Customizes execution to underlying hardware Optimizes locking performance Asynchronous compilation thread JIT Compiler Garbage Collector Application Threads Manages memory on behalf of the application Must balance throughput against observed pauses Exploits many multiple hardware threads Java software threads are executed on multiple hardware threads Thread safe libraries with scalable concurrency support for parallel programming

Configurable Garbage Collection policies Multiple policies to match varying user requirements Pause time, Throughput, Memory footprint and GC overhead All modes exploit parallel execution Dynamic adaptation to number of available hardware cores & threads GC scalability independent from user application scalability Very low overhead (<3%) on typical workloads

How do GC policies compare? - optthruput Optimize Throughput Highly parallel GC + streamlined application thread execution May cause longer pause times -Xgcpolicy:optthruput Java GC Thread 1 Thread 2 Thread 3 Thread n Time Picture is only illustrative and doesn’t reflect any particular real-life application. The purpose is to show theoretical differences in pause times between GC policies.

How do GC policies compare? - optavgpause Optimize Pause Time GC cleans up concurrently with application thread execution Sacrifice some throughput to reduce average pause times -Xgcpolicy:optavgpause Java GC Concurrent Tracing Thread 1 Thread 2 Thread 3 Thread n Time Picture is only illustrative and doesn’t reflect any particular real-life application. The purpose is to show theoretical differences in pause times between GC policies.

How do GC policies compare? - gencon Balanced Clean up many short-lived objects concurrent with application threads Some pauses needed to collect longer-lived objects -Xgcpolicy:gencon Java Global GC Scavenge GC Concurrent Tracing Thread 1 Thread 2 Thread 3 Thread n Time Picture is only illustrative and doesn’t reflect any particular real-life application. The purpose is to show theoretical differences in pause times between GC policies.

How do GC policies compare? - subpools Scalable Scalable GC focused on the larger multiprocessor machines Improved object allocation algorithm May not be appropriate for small-to-midsize configurations –Xgcpolicy:subpool Uses multiple free lists Tries to predict the size of future allocation requests based on earlier allocation requests. Recreates free lists at the end of each GC based on these predictions. While allocating objects on the heap, free chunks are chosen using a “best fit” method, as against the “first fit” method used in other algorithms. Concurrent marking is disabled

JVM optimizations for multi-core scalability Lock removal across JVM and class libraries java.util.concurrent package optimizations Better working set for cache efficiency Stack allocation Remove/optimize synchronization Thread local storage for send/receive buffers Non-blocking containers Asynch JIT compilation on a separate thread Right-sized application runtimes

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