On one hand, the high cost of memory continues to drive demand for memory efficiency on embedded and general purpose computers. On the other hand, programmers are increasingly turning to managed languages like Java for their functionality, programmability, and reliability. Managed languages, however, are not known for their memory efficiency, creating a tension between productivity and performance. This paper examines the sources and types of memory inefficiencies in a set of Java benchmarks. Although prior work has proposed specific heap data compression techniques, they are typically restricted to one model of inefficiency. This paper generalizes and quantitatively compares previously proposed memorysaving approaches and idealized heap compaction. It evaluates a variety of models based on strict and deep object equality, field value equality, removing bytes that are zero, and compressing fields and arrays with a limited number and range of values. The results show that substantial memory reductions are possible in the Java heap. For example, removing bytes that are zero from arrays is particularly effective, reducing the application’s memory footprint by 41 % on average. We are the first to combine multiple savings models on the heap, which very effectively reduces the application by up to 86%, on average 58%. These results demonstrate that future work should be able to combine a high productivity programming language with memory efficiency.

Uncertain data processing is critical in a wide range of applications such as scientific computation handling data with inevitableerrorsandfinancialdecisionmakingrelyingonhuman provided parameters. While increasingly studied in the area of databases, uncertain data processing is often carried out by software, and thus software based solutions are attractive. In particular, Monte Carlo (MC) methods execute software with many samples from the uncertain inputs and observe the statistical behavior of the output. In this paper, we propose a technique to improve the cost-effectiveness of MC methods. Assuming only part of the input is uncertain, the certain part of the input always leads to the same execution across multiple sample runs. We remove such redundancy by coalescing multiple sample runs in a single run. In the coalesced run, the program operates on a vector of values if uncertainty is present and a single value otherwise. We handle cases where control flow and pointers are uncertain. Our results show that we can speed up the execution time of 30 sample runs by an average factor of 2.3 without precision lost or by up to 3.4 with negligible precision lost.

As a safety measure, the Java programming language requires bounds checking of array accesses. This usually translates to dynamic checks each time an array element is accessed. Static analysis can help eliminate some of those checks by proving them to be redundant, reducing the runtime overhead. Compilation of Java programs is usually method-based, and dynamic dispatch complicates interprocedural analysis. The result is a severely restricted static analysis. This paper presents a novel combination of two techniques to alleviate this problem. By assuming constraints that cannot safely be inferred from the program, the amount of provable safe bounds can be greatly extended. These constraints, called speculations, can be derived automatically from the program code by an analyzer, which assumes that there will be no violation of the array bounds. To ensure that the speculations hold at runtime, additional checks have to be injected into the code. Finding good speculations that benefit the runtime performance can be expensive. This paper shows that the speculation technique can be combined with a verifiable annotation framework, allowing most of the work to be shifted to compile-time. Experimental results show that this combination of techniques increases the number of eliminated bounds checks and can result in speedups that approach unconditional bounds check removal.

As part of its type-safety regime, the Java semantics require precise exceptions at runtime when programs attempt out-of-bound array accesses. This paper describes a Java implementation that utilizes a multiphase approach to identifying safe array accesses. This approach reduces runtime overhead by spreading the out-of-bounds checking effort across three phases of compilation and execution: production of mobile code from source code, JIT compilation in the virtual machine, and application code execution. The code producer uses multiple passes (including common subexpression elimination, load elimination, induction variable substitution, speculation of dynamically-verified invariants, and inequality constraint analysis) to identify and prove redundancy of bounds checks. During class-loading and JIT compilation, the virtual machine verifies the proofs, inserts code to dynamically validate speculated invariants, and generates code specialized under the assumption that the speculated invariants hold. At runtime, the method parameters and other inputs are checked against the speculated invariants, and execution reverts to unoptimized code if the speculated invariants do not hold. The combined effect of the multiple phases is to shift the effort associated with bounds-checking array access to phases that are executed earlier and less frequently, thus, reducing runtime overhead. Experimental results show that this approach is able to eliminate more bounds checks than prior approaches with minimal overhead during JIT compilation. These results also show the contribution of each of the passes to the overall elimination. Furthermore, using our multiphase bounds check elimination method increased the speed at which the benchmarks executed by up to 16%. 1