GPUVerify: A Verifier for GPU Kernels

We present a technique for verifying race- and divergence-freedom of GPU kernels that are written in mainstream ker-nel programming languages such as OpenCL and CUDA. Our approach is founded on a novel formal operational se-mantics for GPU programming termed synchronous, delayed visibility (SDV) semantics. The SDV semantics provides a precise definition of barrier divergence in GPU kernels and allows kernel verification to be reduced to analysis of a sequential program, thereby completely avoiding the need to reason about thread interleavings, and allowing existing modular techniques for program verification to be leveraged. We describe an efficient encoding for data race detection and propose a method for automatically inferring loop invari-ants required for verification. We have implemented these techniques as a practical verification tool, GPUVerify, which can be applied directly to OpenCL and CUDA source code. We evaluate GPUVerify with respect to a set of 163 kernels drawn from public and commercial sources. Our evaluation demonstrates that GPUVerify is capable of efficient, auto-matic verification of a large number of real-world kernels

Concurrent Data Structures Linked in Time

Arguments about correctness of a concurrent data structure are typically carried out by using the notion of linearizability and specifying the linearization points of the data structure's procedures. Such arguments are often cumbersome as the linearization points' position in time can be dynamic (depend on the interference, run-time values and events from the past, or even future), non-local (appear in procedures other than the one considered), and whose position in the execution trace may only be determined after the considered procedure has already terminated. In this paper we propose a new method, based on a separation-style logic, for reasoning about concurrent objects with such linearization points. We embrace the dynamic nature of linearization points, and encode it as part of the data structure's auxiliary state, so that it can be dynamically modified in place by auxiliary code, as needed when some appropriate run-time event occurs. We name the idea linking-in-time, because it reduces temporal reasoning to spatial reasoning. For example, modifying a temporal position of a linearization point can be modeled similarly to a pointer update in separation logic. Furthermore, the auxiliary state provides a convenient way to concisely express the properties essential for reasoning about clients of such concurrent objects. We illustrate the method by verifying (mechanically in Coq) an intricate optimal snapshot algorithm due to Jayanti, as well as some clients

AsyncShock: Exploiting Synchronisation Bugs in Intel SGX Enclaves

Intel’s Software Guard Extensions (SGX) provide a new hardware-based trusted execution environment on Intel CPUs using secure enclaves that are resilient to accesses by privileged code and physical attackers. Originally designed for securing small services, SGX bears promise to protect complex, possibly cloud-hosted, legacy applications. In this paper, we show that previously considered harmless synchronisation bugs can turn into severe security vulnerabilities when using SGX. By exploiting use-after-free and time-of-check-to-time-of-use (TOCTTOU) bugs in enclave code, an attacker can hijack its control flow or bypass access control. We present AsyncShock, a tool for exploiting synchronisation bugs of multithreaded code running under SGX. AsyncShock achieves this by only manipulating the scheduling of threads that are used to execute enclave code. It allows an attacker to interrupt threads by forcing segmentation faults on enclave pages. Our evaluation using two types of Intel Skylake CPUs shows that AsyncShock can reliably exploit use-after-free and TOCTTOU bugs

Towards Concurrent Quantitative Separation Logic

In this paper, we develop a novel verification technique to reason about programs featuring concurrency, pointers and randomization. While the integration of concurrency and pointers is well studied, little is known about the combination of all three paradigms. To close this gap, we combine two kinds of separation logic - Quantitative Separation Logic and Concurrent Separation Logic - into a new separation logic that enables reasoning about lower bounds of the probability to realise a postcondition by executing such a program

A load-sharing architecture for high performance optimistic simulations on multi-core machines

In Parallel Discrete Event Simulation (PDES), the simulation model is partitioned into a set of distinct Logical Processes (LPs) which are allowed to concurrently execute simulation events. In this work we present an innovative approach to load-sharing on multi-core/multiprocessor machines, targeted at the optimistic PDES paradigm, where LPs are speculatively allowed to process simulation events with no preventive verification of causal consistency, and actual consistency violations (if any) are recovered via rollback techniques. In our approach, each simulation kernel instance, in charge of hosting and executing a specific set of LPs, runs a set of worker threads, which can be dynamically activated/deactivated on the basis of a distributed algorithm. The latter relies in turn on an analytical model that provides indications on how to reassign processor/core usage across the kernels in order to handle the simulation workload as efficiently as possible. We also present a real implementation of our load-sharing architecture within the ROme OpTimistic Simulator (ROOT-Sim), namely an open-source C-based simulation platform implemented according to the PDES paradigm and the optimistic synchronization approach. Experimental results for an assessment of the validity of our proposal are presented as well

Verifikation Nicht-blockierender Datenstrukturen mit Manueller Speicherverwaltung

Verification of concurrent data structures is one of the most challenging tasks in software verification. The topic has received considerable attention over the course of the last decade. Nevertheless, human-driven techniques remain cumbersome and notoriously difficult while automated approaches suffer from limited applicability. This is particularly true in the absence of garbage collection. The intricacy of non-blocking manual memory management (manual memory reclamation) paired with the complexity of concurrent data structures has so far made automated verification prohibitive. We tackle the challenge of automated verification of non-blocking data structures which manually manage their memory. To that end, we contribute several insights that greatly simplify the verification task. The guiding theme of those simplifications are semantic reductions. We show that the verification of a data structure's complicated target semantics can be conducted in a simpler and smaller semantics which is more amenable to automatic techniques. Some of our reductions rely on good conduct properties of the data structure. The properties we use are derived from practice, for instance, by exploiting common programming patterns. Furthermore, we also show how to automatically check for those properties under the smaller semantics. The main contributions are: (i) A compositional verification approach that verifies the memory management and the data structure separately. (ii) A notion of weak ownership that applies when memory is reclaimed and reused, bridging the gap between garbage collection and manual memory management (iii) A notion of pointer races and harmful ABAs the absence of which ensures that the memory management does not influence the data structure, i.e., it behaves as if executed under garbage collection. Notably, we show that a check for pointer races and harmful ABAs only needs to consider executions where at most a single address is reused. (iv) A notion of strong pointer races the absence of which entails the absence of ordinary pointer races and harmful ABAs. We devise a highly-efficient type check for strong pointer races. After a successful type check, the actual verification can be performed under garbage collection using an off-the-shelf verifier. (v) Experimental evaluations of the aforementioned contributions. We are the first to fully automatically verify practical non-blocking data structures with manual memory management.Verifikation nebenläufiger Datenstrukturen ist eine der herausforderndsten Aufgaben der Programmverifikation. Trotz vieler Beiträge zu diesem Thema, bleiben die existierenden manuellen Techniken mühsam und kompliziert in der Anwendung. Auch automatisierte Verifikationsverfahren sind nur eingeschränkt anwendbar. Diese Schwächen sind besonders ausgeprägt, wenn sich Programme nicht auf einen Garbage-Collector verlassen. Die Komplexität manueller Speicherverwaltung gepaart mit komplexen nicht-blockierenden Datenstrukturen macht die automatisierte Programmverifikation derzeit unmöglich. Diese Arbeit betrachtet die automatisierte Verifikation nicht-blockierender Datenstrukturen, welche ihren Speicher manuell verwalten. Dazu werden Konzepte vorgestellt, die die Verifikation stark vereinfachen. Das Leitmotiv dabei ist die semantische Reduktion, welche die Verifikation in einer leichteren Semantik erlaubt, ohne die eigentliche komplexere Semantik zu betrachten. Einige dieser Reduktion beruhen auf einem Wohlverhalten des zu verifizierenden Programms. Dabei wird das Wohlverhalten mit Bezug auf praxisnahe Eigenschaften definiert, wie sie z.B. von gängigen Programmiermustern vorgegeben werden. Ferner wird gezeigt, dass die Wohlverhaltenseigenschaften ebenfalls unter der einfacheren Semantik nachgewiesen werden können. Die Hauptresultate der vorliegenden Arbeit sind die Folgenden: (i) Ein kompositioneller Verifikationsansatz, welcher Speicherverwaltung und Datenstruktur getrennt verifiziert. (ii) Ein Begriff des Weak-Ownership, welcher selbst dann Anwendung findet, wenn Speicher wiederverwendet wird. (iii) Ein Begriff des Pointer-Race und des Harmful-ABA, deren Abwesenheit garantiert, dass die Speicherverwaltung keinen Einfluss auf die Datenstruktur ausübt und somit unter der Annahme von Garbage-Collection verifiziert werden kann. Bemerkenswerterweise genügt es diese Abwesenheit unter Reallokation nur einer fixex Speicherzelle zu prüfen. (iv) Ein Begriff des Strong-Pointer-Race, dessen Abwesenheit sowohl Pointer-Races als auch Harmful-ABA ausschließt. Um ein Programm auf Strong-Pointer-Races zu prüfen, präsentieren wir ein Typsystem. Ein erfolgreicher Typcheck erlaubt die tatsächlich zu überprüfende Eigenschaft unter der Annahme eines Garbage-Collectors nachzuweisen. (v) Experimentelle Evaluationen. Die vorgestellten Techniken sind die Ersten, die nicht-blockierende Datenstrukturen mit gängigen Speicherverwaltungen vollständig automatisch verifizieren können