Dynamic software updating (DSU) systems eliminate costly downtime by dynamically fixing bugs and adding features to executing programs. Given a static code patch, most DSU systems construct runtime code changes automatically. However, a dynamic update must also specify how to change the running program's execution state, e.g., the stack and heap, to make it compatible with the new code. Constructing such state transformations correctly and automatically remains an open problem. This paper presents a solution called Targeted Object Synthesis (TOS). TOS first executes the same tests on the old and new program versions separately, observing the program heap state at a few corresponding points. Given two corresponding heap states, TOS matches objects in the two versions using key fields that uniquely identify objects and correlate old and new-version objects. Given example object pairs, TOS then synthesizes the simplest-possible function that transforms an old-version object to its new-version counterpart. We show that TOS is effective on updates to four open-source server programs for which it generates non-trivial transformation functions that use conditionals, operate on collections, and fix memory leaks. These transformations help programmers understand their changes and apply dynamic software updates.

Software evolves to fix bugs and add features. Stopping and restarting programs to apply changes is inconvenient and often costly. Dynamic software updating (DSU) addresses this problem by updating programs while they execute, but existing DSU systems for managed languages do not support many updates that occur in practice and are inefficient. This paper presents the design and implementation of JVOLVE, a DSU-enhanced Java VM. Updated programs may add, delete, and replace fields and methods anywhere within the class hierarchy. JVOLVE implements these updates by adding to and coordinating VM classloading, just-in-time compilation, scheduling, return barriers, on-stack replacement, and garbage collection. JVOLVE is safe: its use of bytecode verification and VM thread synchronization ensures that an update will always produce type-correct executions. JVOLVE is flexible: it can support 20 of 22 updates to three open-source programs-Jetty web server, JavaE-mailServer, and CrossFTP server-based on actual releases occurring over 1 to 2 years. JVOLVE is efficient: performance experiments show that JVOLVE incurs no overhead during steady-state execution. These results demonstrate that this work is a significant step towards practical support for dynamic updates in virtual machines for managed languages.

Dynamic software updating (DSU) systems eliminate costly downtime by dynamically fixing bugs and adding features to executing programs. Given a static code patch, most DSU systems construct runtime code changes automatically. However, a dynamic update must also specify how to change the running program's execution state, e.g., the stack and heap, to make it compatible with the new code. Constructing such state transformations correctly and automatically remains an open problem. This paper presents a solution called Targeted Object Synthesis (TOS). TOS first executes the same tests on the old and new program versions separately, observing the program heap state at a few corresponding points. Given two corresponding heap states, TOS matches objects in the two versions using key fields that uniquely identify objects and correlate old and new-version objects. Given example object pairs, TOS then synthesizes the simplest-possible function that transforms an old-version object to its new-version counterpart. We show that TOS is effective on updates to four open-source server programs for which it generates non-trivial transformation functions that use conditionals, operate on collections, and fix memory leaks. These transformations help programmers understand their changes and apply dynamic software updates.

International audienceExposing more instruction-level parallelism in out-of-order superscalar processors requires increasing the number of dynamic in-flight instructions. However, large instruction windows increase power consumption and latency in the issue logic. We propose a design called Hybrid Dataflow Graph Execution (HeDGE) for conventional Instruction Set Architectures (ISAs). HeDGE explicitly maintains dependences between instructions in the issue window by modifying the issue, register renaming, and wakeup logic. The HeDGE wakeup logic notifies only consumer instructions when data values arrive. Explicit consumer encoding naturally leads to the use of Random Access Memory (RAM) instead of Content Addressable Memory (CAM) needed for broadcast. HeDGE is distinguished from prior approaches in part because it dynamically inserts forwarding instructions. Although these additional instructions degrade performance by an average of 3 to 17% for SPEC C and Fortran benchmarks and 1.5% to 8% for DaCapo Java benchmarks, they enable energy efficient execution in large instruction windows. The HeDGE RAM-based instruction window consumes on average 98% less energy than a conventional CAM as modeled in CACTI for 70nm technology. In conventional designs, this structure contributes 7 to 20% to total energy consumption. HeDGE allows us to achieve power and energy gains by using RAMs in the issue logic while maintaining a conventional instruction set