Data collections provide a powerful abstraction to organize data, simplifying development and maintenance. Choosing an implementation for each collection is a critical decision, with performance, memory and energy tradeoffs that need to be balanced for each use case. Specialized implementations offer significant benefits over their general-purpose counterparts, but also require certain properties of the data they store, such as uniqueness or ordering. To employ them, developers must either possess domain knowledge or transform their data to exhibit the desired property, which is a tedious, manual process. One such transformation—commonly used in data mining and program analysis—is data enumeration, where data items are assigned unique identifiers to enable fast equality checks and compact memory layout. In this paper, we present an automated approach to data enumeration, eliminating the need for manual developer effort. Our implementation in the MemOIR compiler achieves speedups of 2.16× on average (up to 8.72×) and reduces peak memory consumption by 5.6% on average (up to 50.7%). This work shows that automated techniques can manufacture data properties to unlock specialized collection implementations, pushing the envelope of collection-oriented optimization.

Tiny devices have become ubiquitous in people's daily lives. Their applications dictate tight energy budgets, but also require reasonable performance to meet user expectations. To this end, the hardware of tiny devices has been highly optimized, making further optimizations difficult. In this work, we identify a missed opportunity: the bitwidth selection of program variables. Today's compilers directly translate the bitwidth specified in the source code to the binary. However, we observe that most variables do not utilize the full bitwidth specified in the source code for the majority of execution. To leverage this opportunity, we propose BitSpec : a system that performs fine-grained speculation on the bitwidth of program variables. BitSpec is implemented as a compiler-architecture co-design, where the compiler transparently reduces the bitwidth of program variables to their expected needs and the hardware monitors speculative variables, reporting misspeculation to the software, which re-executes at the original bitwidth, ensuring correctness. BitSpec reduces energy consumption by 9.9% on average, up to 28.2% .

The inability to relocate objects in unmanaged languages brings with it a menagerie of problems. Perhaps the most impactful is memory fragmentation, which has long plagued applications such as databases and web servers. These issues either fester or require Herculean programmer effort to address on a per-application basis because, in general, heap objects cannot be moved in unmanaged languages. In contrast, managed languages like C# cleanly address fragmentation through the use of compacting garbage collection techniques built upon heap object movement. In this work, we bridge this gap between unmanaged and managed languages through the use of handles, a level of indirection allowing heap object movement. Handles open the door to seamlessly employing runtime features from managed languages in existing, unmodified code written in unmanaged languages. We describe a new compiler and runtime system, Alaska, that acts as a drop-in replacement for malloc. Without any programmer effort, the Alaska compiler transforms pointer-based code to utilize handles, with optimizations to minimize performance impact. A codesigned runtime system manages this new level of indirection and exploits heap object movement via an extensible service interface. We investigate the overheads of Alaska on large benchmarks and applications spanning multiple domains. To show the power and extensibility of handles, we use Alaska to eliminate fragmentation on the heap through defragmentation, reducing memory usage by up to 40% in Redis.

Compiler research and development has treated computation as the primary driver of performance improvements in C/C++ programs, leaving memory optimizations as a secondary consideration. Developers are currently handed the arduous task of describing both the semantics and layout of their data in memory, either manually or via libraries, prematurely lowering high-level data collections to a low-level view of memory for the compiler. Thus, the compiler can only glean conservative information about the memory in a program, e.g., alias analysis, and is further hampered by heavy memory optimizations. This paper proposes the Memory Object Intermediate Representation (MEMOIR), a language-agnostic SSA form for sequential and associative data collections, objects, and the fields contained therein. At the core of Memoir is a decoupling of the memory used to store data from that used to logically organize data. Through its SSA form, Memoir compilers can perform element-level analysis on data collections, enabling static analysis on the state of a collection or object at any given program point. To illustrate the power of this analysis, we perform dead element elimination, resulting in a 26.6% speedup on mcf from SPECINT 2017. With the degree of freedom to mutate memory layout, our Memoir compiler performs field elision and dead field elimination, reducing peak memory usage of mcf by 20.8%.

Modern programming languages offer abstractions that simplify software development and allow hardware to reach its full potential. These abstractions range from the well-established OpenMP language extensions to newer C++ features like smart pointers. To properly use these abstractions in an existing codebase, programmers must determine how a given source code region interacts with Program State Elements (PSEs) (i.e., the program's variables and memory locations). We call this process Program State Element Characterization (PSEC). Without tool support for PSEC, a programmer's only option is to manually study the entire codebase. We propose a profile-based approach that automates PSEC and provides abstraction recommendations to programmers. Because a profile-based approach incurs an impractical overhead, we introduce the Compiler and Runtime Memory Observation Tool (CARMOT), a PSEC-specific compiler co-designed with a parallel runtime. CARMOT reduces the overhead of PSEC by two orders of magnitude, making PSEC practical. We show that CARMOT's recommendations achieve the same speedup as hand-tuned OpenMP directives and avoid memory leaks with C++ smart pointers. From this, we argue that PSEC tools, such as CARMOT, can provide support for the rich ecosystem of modern programming language abstractions.

Modern and emerging architectures demand increasingly complex compiler analyses and transformations. As the emphasis on compiler infrastructure moves beyond support for peephole optimizations and the extraction of instruction-level parallelism, compilers should support custom tools designed to meet these demands with higher-level analysis-powered abstractions and functionalities of wider program scope. This paper introduces NOELLE, a robust open-source domain-independent compilation layer built upon LLVM providing this support. NOELLE extends abstractions and functionalities provided by LLVM enabling advanced, program-wide code analyses and transformations. This paper shows the power of NOELLE by presenting a diverse set of 11 custom tools built upon it.