Application slowdowns that accumulate on the critical path significantly impact performance. Identifying them requires visualizing inter-thread wait-for dependencies, a task neglected by existing tools, especially regarding ad-hoc, busy-waiting synchronization. We present Tapestry, which efficiently traces these dependencies by dynamically switching between hardware watchpoints and software breakpoints. Using three use cases, we show that Tapestry is able to reveal NUMA effects and explain performance anomalies in oversubscribed and virtualized environments.

Performance-oriented applications require efficient locks to harness the computing power of multicore architectures. While fast, spinlock algorithms suffer severe performance degradation when thread counts exceed available hardware capacity, i.e., in oversubscribed scenarios. Existing solutions rely on imprecise heuristics for blocking, leading to suboptimal performance. We present FlexGuard, the first approach that systematically switches from busy-waiting to blocking precisely when a lock-holding thread is preempted. Flex-Guard achieves this by communicating with the OS scheduler via eBPF, unlike prior approaches. FlexGuard matches or improves performance in LevelDB, a memory-optimized database index, PARSEC's Dedup, and SPLASH2X's Raytrace and Streamcluster, boosting throughput by 1–6× in non-oversubscribed and up to 5× in oversubscribed scenarios.

Reachability queries checking the existence of a path from a source node to a target node are fundamental operators for querying and processing graph data. Current approaches for index-based evaluation of reachability queries either focus on plain reachability or constraint-based reachability with the alternation of edge labels only. In this paper, we study for the first time the problem of index-based processing for recursive label-concatenated reachability queries, referred to as RLC queries. These queries check the existence of a path that can satisfy the constraint defined by a concatenation of at most k edge labels under the Kleene plus. Many practical graph database and network analysis applications exhibit RLC queries. However, their evaluation remains prohibitive in current graph database engines.We introduce the RLC index, the first reachability index to efficiently process RLC queries. The RLC index checks whether the source vertex can reach an intermediate vertex that can also reach the target vertex under a recursive label-concatenated constraint. We propose an indexing algorithm to build the RLC index, which guarantees the soundness and the completeness of query execution and avoids recording redundant index entries. Comprehensive experiments on real-world graphs show that the RLC index can significantly reduce both the offline processing cost and the memory overhead of computing and storing transitive closure, while improving query processing up to six orders of magnitude over online traversals. Finally, our open-source implementation of the RLC index significantly outperforms current mainstream graph engines for evaluating RLC queries.

Trusted execution environments like Intel SGX provide enclaves, which offer strong security guarantees for applications. Running entire applications inside enclaves is possible, but this approach leads to a large trusted computing base (TCB). As such, various tools have been developed to partition programs written in languages such as C or Java into trusted and untrusted parts, which are run in and out of enclaves respectively. However, those tools depend on language-specific taint-analysis and partitioning techniques. They cannot be reused for other languages and there is thus a need for tools that transcend this language barrier.

To best support highly parallel applications, Linux's CFS scheduler tends to spread tasks across the machine on task creation and wakeup. It has been observed, however, that in a server environment, such a strategy leads to tasks being unnecessarily placed on long-idle cores that are running at lower frequencies, reducing performance, and to tasks being unnecessarily distributed across sockets, consuming more energy. In this paper, we propose to exploit the principle of core reuse, by constructing a nest of cores to be used in priority for task scheduling, thus obtaining higher frequencies and using fewer sockets. We implement the Nest scheduler in the Linux kernel. While performance and energy usage are comparable to CFS for highly parallel applications, for a range of applications using fewer tasks than cores, Nest improves performance 10%--2× and can reduce energy usage.

The popularity of the Java programming language has led to its wide adoption in cloud computing infrastructures. However, Java applications running in untrusted clouds are vulnerable to various forms of privileged attacks. The emergence of trusted execution environments (TEEs) such as Intel SGX mitigates this problem. TEEs protect code and data in secure enclaves inaccessible to untrusted software, including the kernel and hypervisors. To efficiently use TEEs, developers must manually partition their applications into trusted and untrusted parts, in order to reduce the size of the trusted computing base (TCB) and minimise the risks of security vulnerabilities. However, partitioning applications poses two important challenges: (i) ensuring efficient object communication between the partitioned components, and (ii) ensuring the consistency of garbage collection between the parts, especially with memory-managed languages such as Java. We present Montsalvat, a tool which provides a practical and intuitive annotation-based partitioning approach for Java applications destined for secure enclaves. Montsalvat provides an RMI-like mechanism to ensure inter-object communication, as well as consistent garbage collection across the partitioned components. We implement Montsalvat with GraalVM native-image, a tool for compiling Java applications ahead-of-time into standalone native executables that do not require a JVM at runtime. Our extensive evaluation with micro- and macro-benchmarks shows our partitioning approach to boost performance in real-world applications up to 6.6x (PalDB) and 2.2x (GraphChi) as compared to solutions that naively include the entire applications in the enclave.

Recent research and bug reports have shown that work conservation, the property that a core is idle only if no other core is overloaded, is not guaranteed by Linux's CFS or FreeBSD's ULE multicore schedulers. Indeed, multicore schedulers are challenging to specify and verify: they must operate under stringent performance requirements, while handling very large numbers of concurrent operations on threads. As a consequence, the verification of correctness properties of schedulers has not yet been considered.

The graph model enables a broad range of analysis, thus graph processing is an invaluable tool in data analytics. At the heart of every graph-processing system lies a concurrent graph data structure storing the graph. Such a data structure needs to be highly efficient for both graph algorithms and queries. Due to the continuous evolution, the sparsity, and the scale-free nature of real-world graphs, graph-processing systems face the challenge of providing an appropriate graph data structure that enables both fast analytical workloads and low-memory graph mutations. Existing graph structures offer a hard trade-off between read-only performance, update friendliness, and memory consumption upon updates. In this paper, we introduce CSR++, a new graph data structure that removes these trade-offs and enables both fast read-only analytics and quick and memory-friendly mutations. CSR++ combines ideas from CSR, the fastest read-only data structure, and adjacency lists to achieve the best of both worlds. We compare CSR++ to CSR, adjacency lists from the Boost Graph Library, and LLAMA, a state-of-the-art update-friendly graph structure. In our evaluation, which is based on popular graph-processing algorithms executed over real-world graphs, we show that CSR++ remains close to CSR in read-only concurrent performance (within 10% on average), while significantly outperforming CSR (by an order of magnitude) and LLAMA (by almost 2×) with frequent updates.

The complexity of computer architectures has risen since the early years of the Linux kernel: Simultaneous Multi-Threading (SMT), multicore processing, and frequency scaling with complex algorithms such as Intel® Turbo Boost have all become omnipresent. In order to keep up with hardware innovations, the Linux scheduler has been rewritten several times, and many hardware-related heuristics have been added. Despite this, we show in this paper that a fundamental problem was never identified: the POSIX process creation model, i.e., fork/wait, can behave inefficiently on current multicore architectures due to frequency scaling. We investigate this issue through a simple case study: the compilation of the Linux kernel source tree. To do this, we develop SchedLog, a low-overhead scheduler tracing tool, and SchedDisplay, a scriptable tool to graphically analyze SchedLog's traces efficiently.

This paper introduces smart arrays, an abstraction for providing adaptive and efficient language-independent data storage. Their smart functionalities include NUMA-aware data placement across sockets and bit compression. We show how our single C++ implementation can be used efficiently from both native C++ and compiled Java code. We experimentally evaluate smart arrays on a diverse set of C++ and Java analytics workloads. Further, we show how their smart functionalities affect performance and lead to differences in hardware resource demands on multicore machines, motivating the need for adaptivity. We observe that smart arrays can significantly decrease the memory space requirements of analytics workloads, and improve their performance by up to 4x. Smart arrays are the first step towards general smart collections with various smart functionalities that enable the consumption of hardware resources to be traded-off against one another.

Operating systems have been shown to waste machine resources by leaving cores idle while work is ready to be scheduled. This results in suboptimal performance for user applications, and wasted power.

As a central part of resource management, the OS thread scheduler must maintain the following, simple, invariant: make sure that ready threads are scheduled on available cores. As simple as it may seem, we found that this invariant is often broken in Linux. Cores may stay idle for seconds while ready threads are waiting in runqueues. In our experiments, these performance bugs caused many-fold performance degradation for synchronization-heavy scientific applications, 13% higher latency for kernel make, and a 14-23% decrease in TPC-H throughput for a widely used commercial database. The main contribution of this work is the discovery and analysis of these bugs and providing the fixes. Conventional testing techniques and debugging tools are ineffective at confirming or understanding this kind of bugs, because their symptoms are often evasive. To drive our investigation, we built new tools that check for violation of the invariant online and visualize scheduling activity. They are simple, easily portable across kernel versions, and run with a negligible overhead. We believe that making these tools part of the kernel developers' tool belt can help keep this type of bug at bay.

Omitting resource-release operations in systems error handling code can lead to memory leaks, crashes, and deadlocks. Finding omission faults is challenging due to the difficulty of reproducing system errors, the diversity of system resources, and the lack of appropriate abstractions in the C language. To address these issues, numerous approaches have been proposed that globally scan a code base for common resource-release operations. Such macroscopic approaches are notorious for their many false positives, while also leaving many faults undetected. We propose a novel microscopic approach to finding resource-release omission faults in systems software. Rather than generalizing from the entire source code, our approach focuses on the error-handling code of each function. Using our tool, Hector, we have found over 370 faults in six systems software projects, including Linux, with a 23% false positive rate. Some of these faults allow an unprivileged malicious user to crash the entire system.