There is a growing need for efficient distributed computing for transaction processing. One of the key requirements for runtime systems in distributed environments is fault tolerance. Such a system needs to preserve the data consistency at transaction boundaries so as to resume the ongoing tasks from checkpoints with consistent data for any component failure. Another key requirement is that the system needs to be lightweight enough in normal execution to provide scalable performance. This paper presents the design and implementation of a new fault tolerant execution framework that addresses both of these requirements. We replicate each partition of the distributed persistent data on three nodes (triplet) with two different types of backups, one using warm replication and the other using cold replication. For node failures, the system is automatically recoverable unless all three nodes in any triplet fail at the same time. The system tolerates simultaneous two-node failures in any triplet most of the cases. We obtained a new trade-off in that 43% performance improvements can be achieved by slightly compromising the system availability.

A scale-out system is a cluster of commodity machines, and offers a good platform to support steadily increasing workloads that process growing data sets. Sharding [4] is a method of partitioning data and processing a computation on a scale-out system. In a database system, a large table can be partitioned into small tables so each node can process its part of the computation. The sharding approach in a large batch transaction processing, which is important in financial area, presents two hard problems to programmers. Programmers have to write complex code (1) to transfer the input data so as to align the computations with the data partitions, and (2) to manage the distributed transactions. This paper presents a new parallel programming framework that makes parallel transactional programming easier by specifying transaction scopes and partitioners to simplify the code. Transaction scopes include series of subtransactions, each of which performs local operations. The system manages the distributed transactions automatically. A partitioner represents how the computation should be decomposed and aligned with the data partitions to avoid remote database accesses. Between paired of subtransactions, the system handles the data shuffling across the network. We implemented our parallel programming framework as a new Java class library. We hide all of the complex details of data transfer and distributed transaction management in the library. Our programming framework can eliminate almost 66% of the lines of code compared to a current programming approach without programming framework support. We also confirmed good scalability, with a scaling factor of 20.6 on 24 nodes using our modified batch program for the TPC-C benchmark.

Method inlining and data flow analysis are two major optimization components for effective program transformations, but they often suffer from the existence of rarely or never executed code contained in the target method. One major problem lies in the assumption that the compilation unit is partitioned at method boundaries. This article describes the design and implementation of a region-based compilation technique in our dynamic optimization framework, in which the compiled regions are selected as code portions without rarely executed code. The key parts of this technique are the region selection, partial inlining, and region exit handling. For region selection, we employ both static heuristics and dynamic profiles to identify and eliminate rare sections of code. The region selection process and method inlining decisions are interwoven, so that method inlining exposes other targets for region selection, while the region selection in the inline target conserves the inlining budget, allowing more method inlining to be performed. The inlining process can be performed for parts of a method, not just for the entire body of the method. When the program attempts to exit from a region boundary, we trigger recompilation and then use on-stack replacement to continue the execution from the corresponding entry point in the recompiled code. We have implemented these techniques in our Java JIT compiler, and conducted a comprehensive evaluation. The experimental results show that our region-based compilation approach achieves approximately 4% performance improvement on average, while reducing the compilation overhead by 10% to 30%, in comparison to the traditional method-based compilation techniques.

The high performance implementation of Java Virtual Machines (JVM) and Just-In-Time (JIT) compilers is directed toward employing a dynamic compilation system on the basis of online runtime profile information. The trade-off between the compilation overhead and performance benefit is a crucial issue for such a system. This article describes the design and implementation of a dynamic optimization framework in a production-level Java JIT compiler, together with two techniques for profile-directed optimizations: method inlining and code specialization. Our approach is to employ a mixed mode interpreter and a three-level optimizing compiler, supporting level-1 to level-3 optimizations, each of which has a different set of trade-offs between compilation overhead and execution speed. A lightweight sampling profiler operates continuously during the entire period while applications are running to monitor the programs' hot spots. Detailed information on runtime behavior can be collected by dynamically generating instrumentation code that is installed to and uninstalled from the specified recompilation target code. Value profiling with this instrumentation mechanism allows fully automatic profile-directed method inlining and code specialization to be performed on the basis of call site information or specific parameter values at the higher optimization levels. The experimental results show that our approach offers high performance and low compilation overhead in both program startup and steady state measurements in comparison to the previous systems. The two profile-directed optimization techniques contribute significant portions of the improvements.

This paper describes the system overview of our Java Just-In-Time (JIT) compiler, which is the basis for the latest production version of IBM Java JIT compiler that supports a diversity of processor architectures including both 32-bit and 64-bit modes, CISC, RISC, and VLIW architectures. In particular, we focus on the design and evaluation of the cross-platform optimizations that are common across different architectures. We studied the effectiveness of each optimization by selectively disabling it in our JIT compiler on three different platforms: IA-32, IA-64, and PowerPC. Our detailed measurements allowed us to rank the optimizations in terms of the greatest performance improvements with the smallest compilation times. The identified set includes method inlining only for tiny methods, exception check eliminations using forward dataflow analysis and partial redundancy elimination, scalar replacement for instance and class fields using dataflow analysis, optimizations for type inclusion checks, and the elimination of merge points in the control flow graphs. These optimizations can achieve 90% of the peak performance for two industry-standard benchmark programs on these platforms with only 34% of the compilation time compared to the case for using all of the optimizations.

Method inlining and data flow analysis are two major optimization components for effective program transformations, however they often suffer from the existence of rarely or never executed code contained in the target method. One major problem lies in the assumption that the compilation unit is partitioned at method boundaries. This paper describes the design and implementation of a region-based compilation technique in our dynamic compilation system, in which the compiled regions are selected as code portions without rarely executed code. The key part of this technique is the region selection, partial inlining, and region exit handling. For region selection, we employ both static heuristics and dynamic profiles to identify rare sections of code. The region selection process and method inlining decision are interwoven, so that method inlining exposes other targets for region selection, while the region selection in the inline target conserves the inlining budget, leading to more method inlining. Thus the inlining process can be performed for parts of a method, not for the entire body of the method. When the program attempts to exit from a region boundary, we trigger recompilation and then rely on on-stack replacement to continue the execution from the corresponding entry point in the recompiled code. We have implemented these techniques in our Java JIT compiler, and conducted a comprehensive evaluation. The experimental results show that the approach of region-based compilation achieves approximately 5% performance improvement on average, while reducing the compilation overhead by 20 to 30%, in comparison to the traditional function-based compilation techniques.

The high performance implementation of Java Virtual Machines (JVM) and just-in-time (JIT) compilers is directed toward adaptive compilation optimizations on the basis of online runtime profile information. This paper describes the design and implementation of a dynamic optimization framework in a production-level Java JIT compiler. Our approach is to employ a mixed mode interpreter and a three level optimizing compiler, supporting quick, full, and special optimization, each of which has a different set of tradeoffs between compilation overhead and execution speed. a lightweight sampling profiler operates continuously during the entire program's exectuion. When necessary, detailed information on runtime behavior is collected by dynmiacally generating instrumentation code which can be installed to and uninstalled from the specified recompilation target code. Value profiling with this instrumentation mechanism allows fully automatic code specialization to be performed on the basis of specific parameter values or global data at the highest optimization level. The experimental results show that our approach offers high performance and a low code expansion ratio in both program startup and steady state measurements in comparison to the compile-only approach, and that the code specialization can also contribute modest performance improvement

The Java language incurs a runtime overhead for exception checks and object accesses, which are executed without an interior pointer in order to ensure safety. It also requires type inclusion test, dynamic class loading, and dynamic method calls in order to ensure flexibility. A ‘Just-In-Time’ (JIT) compiler generates native code from Java byte code at runtime. It must improve the runtime performance without compromising the safety and flexibility of the Java language. We designed and implemented effective optimizations for a JIT compiler, such as exception check elimination, common subexpression elimination, simple type inclusion test, method inlining, and devirtualization of dynamic method call. We evaluate the performance benefits of these optimizations based on various statistics collected using SPECjvm98, its candidates, and two JavaSoft applications with byte code sizes ranging from 23 000 to 280 000 bytes. Each optimization contributes to an improvement in the performance of the programs. Copyright © 2000 John Wiley & Sons, Ltd.

This paper presents a new technique for detecting and optimizing reduction operations for parallelizhtg compilers.The technique presented here can detect reduction constructs in general complex loops, parallelize the loops containing reduction constructs, and optimize communications for multiple reduction operations.The optimization proposed here can be applied not only to individual reduction loops, but also to multiple loop nests throughout a program.The techniques have been implemented in our HPF compiler, and their effectiveness is evaluated on an IBM Scalable PowerParallel System SP2 using a set of standard benchmarking programs.Although the aperimental results are still preliminary, it is shown that our techniques for detecting and optimizing reductions are eflective on practical application programs.