Remote-scope Promotion: Clarified, Rectified, and Verified

Modern accelerator programming frameworks, such as OpenCL, organise threads into work-groups. Remote-scope promotion (RSP) is a language extension recently proposed by AMD researchers that is designed to enable applications, for the first time, both to optimise for the common case of intra-work-group communication (using memory scopes to provide consistency only within a work-group) and to allow occasional inter-work-group communication (as required, for instance, to support the popular load-balancing idiom of work stealing). We present the first formal, axiomatic memory model of OpenCL extended with RSP. We have extended the Herd memory model simulator with support for OpenCL kernels that exploit RSP, and used it to discover bugs in several litmus tests and a work-stealing queue, that have been used previously in the study of RSP. We have also formalised the proposed GPU implementation of RSP. The formalisation process allowed us to identify bugs in the description of RSP that could result in well-synchronised programs experiencing memory inconsistencies. We present and prove sound a new implementation of RSP that incorporates bug fixes and requires less non-standard hardware than the original implementation. This work, a collaboration between academia and industry, clearly demonstrates how, when designing hardware support for a new concurrent language feature, the early application of formal tools and techniques can help to prevent errors, such as those we have found, from making it into silicon

Loop splitting for efficient pipelining in high-level synthesis

Loop pipelining is widely adopted as a key optimization method in high-level synthesis (HLS). However, when complex memory dependencies appear in a loop, commercial HLS tools are still not able to maximize pipeline performance. In this paper, we leverage parametric polyhedral analysis to reason about memory dependence patterns that are uncertain (i.e., parameterised by an undetermined variable) and/or nonuniform (i.e., varying between loop iterations). We develop an automated source-to-source code transformation to split the loop into pieces, which are then synthesised by Vivado HLS as the hardware generation back-end. Our technique allows generated loops to run with a minimal interval, automatically inserting statically-determined parametric pipeline breaks at those iterations violating dependencies. Our experiments on seven representative benchmarks show that, compared to default loop pipelining, our parametric loop splitting improves pipeline performance by 4:3 in terms of clock cycles per iteration. The optimized pipelines consume 2:0 as many LUTs, 1:8 as many registers, and 1:1 as many DSP blocks. Hence the area-time product is improved by nearly a factor of 2

Weak persistency semantics from the ground up: formalising the persistency semantics of ARMv8 and transactional models

Emerging non-volatile memory (NVM) technologies promise the durability of disks with the performance of volatile memory (RAM). To describe the persistency guarantees of NVM, several memory persistency models have been proposed in the literature. However, the formal persistency semantics of mainstream hardware is unexplored to date. To close this gap, we present a formal declarative framework for describing concurrency models in the NVM context, and then develop the PARMv8 persistency model as an instance of our framework, formalising the persistency semantics of the ARMv8 architecture for the first time. To facilitate correct persistent programming, we study transactions as a simple abstraction for concurrency and persistency control. We thus develop the PSER (persistent serialisability) persistency model, formalising transactional semantics in the NVM context for the first time, and demonstrate that PSER correctly compiles to PARMv8. This then enables programmers to write correct, concurrent and persistent programs, without having to understand the low-level architecture-specific persistency semantics of the underlying hardware

Simulating operational memory models using off-the-shelf program analysis tools

Memory models allow reasoning about the correctness of multithreaded programs. Constructing and using such models is facilitated by simulators that reveal which behaviours of a given program are allowed. While extensive work has been done on simulating axiomatic memory models, there has been less work on simulation of operational models. Operational models are often considered more intuitive than axiomatic models, but are challenging to simulate due to the vast number of paths through the model’s transition system. Observing that a similar path-explosion problem is tackled by program analysis tools, we investigate the idea of reducing the decision problem of “whether a given memory model allows a given behaviour” to the decision problem of “whether a given C program is safe”, which can be handled by a variety of off-the-shelf tools. We report on our experience using multiple program analysis tools for C for this purpose—a model checker (CBMC), a symbolic execution tool (KLEE), and three coverage-guided fuzzers (libFuzzer, Centipede and AFL++)—presenting two case-studies. First, we evaluate the performance and scalability of these tools in the context of the x86 memory model, showing that fuzzers offer performance competitive with that of RMEM, a state-of-the-art bespoke memory model simulator. Second, we study a more complex, recently developed memory model for hybrid CPU/FPGA devices for which no bespoke simulator is available. We highlight how different encoding strategies can aid the various tools and show how our approach allows us to simulate the CPU/FPGA model twice as deeply as in prior work, leading to us finding and fixing several infidelities in the model. We also experimented with applying three analysis tools that won the “falsification” category in the 2023 Annual Software Verification Competition (SV-COMP). We found that these tools do not scale to our use cases, motivating us to submit example C programs arising from our work for inclusion in the set of SV-COMP benchmarks, so that they can serve as challenge examples

Polyhedral-based dynamic loop pipelining for high-level synthesis

Loop pipelining is one of the most important optimization methods in high-level synthesis (HLS) for increasing loop parallelism. There has been considerable work on improving loop pipelining, which mainly focuses on optimizing static operation scheduling and parallel memory accesses. Nonetheless, when loops contain complex memory dependencies, current techniques cannot generate high performance pipelines. In this paper, we extend the capability of loop pipelining in HLS to handle loops with uncertain dependencies (i.e., parameterized by an undetermined variable) and/or nonuniform dependencies (i.e., varying between loop iterations). Our optimization allows a pipeline to be statically scheduled without the aforementioned memory dependencies, but an associated controller will change the execution speed of loop iterations at runtime. This allows the augmented pipeline to process each loop iteration as fast as possible without violating memory dependencies. We use a parametric polyhedral analysis to generate the control logic for when to safely run all loop iterations in the pipeline and when to break the pipeline execution to resolve memory conflicts. Our techniques have been prototyped in an automated source-to-source code transformation framework, with Xilinx Vivado HLS, a leading HLS tool, as the RTL generation backend. Over a suite of benchmarks, experiments show that our optimization can implement optimized pipelines at almost the same clock speed as without our transformations, running approximately 3.7-10× faster, with a reasonable resource overhead

Balancing static islands in dynamically scheduled circuits using continuous petri nets

High-level synthesis (HLS) tools automatically transform a high-level program, for example in C/C++, into a low-level hardware description. A key challenge in HLS is scheduling, i.e. determining the start time of all the operations in the untimed program. A major shortcoming of existing approaches to scheduling – whether they are static (start times determined at compile-time), dynamic (start times determined at run-time), or a hybrid of both – is that the static analysis cannot efficiently explore the run-time hardware behaviours. Existing approaches either assume the timing behaviour in extreme cases, which can cause sub-optimal performance or larger area, or use simulation-based approaches, which take a long time to explore enough program traces. In this article, we propose an efficient approach using probabilistic analysis for HLS tools to efficiently explore the timing behaviour of scheduled hardware. We capture the performance of the hardware using Timed Continous Petri nets with immediate transitions, allowing us to leverage efficient Petri net analysis tools for making HLS decisions. We demonstrate the utility of our approach by using it to automatically estimate the hardware throughput for balancing the throughput for statically scheduled components (also known as static islands) computing in a dynamically scheduled circuit. Over a set of benchmarks, we show that our approach on average incurs a 2% overhead in area-delay product compared to optimal designs by exhaustive search

Balancing Static Islands in Dynamically Scheduled Circuits using Continuous Petri Nets

High-level synthesis (HLS) tools automatically transform a high-level program, for example in C/C++, into a low-level hardware description. A key challenge in HLS is scheduling, i.e. determining the start time of all the operations in the untimed program. A major shortcoming of existing approaches to scheduling – whether they are static (start times determined at compile-time), dynamic (start times determined at run-time), or a hybrid of both – is that the static analysis cannot efficiently explore the run-time hardware behaviours. Existing approaches either assume the timing behaviour in extreme cases, which can cause sub-optimal performance or larger area, or use simulation-based approaches, which take a long time to explore enough program traces. In this article, we propose an efficient approach using probabilistic analysis for HLS tools to efficiently explore the timing behaviour of scheduled hardware. We capture the performance of the hardware using Timed Continous Petri nets with immediate transitions, allowing us to leverage efficient Petri net analysis tools for making HLS decisions. We demonstrate the utility of our approach by using it to automatically estimate the hardware throughput for balancing the throughput for statically scheduled components (also known as static islands) computing in a dynamically scheduled circuit. Over a set of benchmarks, we show that our approach on average incurs a 2% overhead in area-delay product compared to optimal designs by exhaustive search

Hardware synthesis of weakly consistent C concurrency

Lock-free algorithms, in which threads synchronise not via coarse-grained mutual exclusion but via fine-grained atomic operations ('atomics'), have been shown empirically to be the fastest class of multi-threaded algorithms in the realm of conventional processors. This paper explores how these algorithms can be compiled from C to reconfigurable hardware via high-level synthesis (HLS). We focus on the scheduling problem, in which software instructions are assigned to hardware clock cycles. We first show that typical HLS scheduling constraints are insufficient to implement atomics, because they permit some instruction reorderings that, though sound in a single-threaded context, demonstrably cause erroneous results when synthesising multi-threaded programs. We then show that correct behaviour can be restored by imposing additional intra-thread constraints among the memory operations. We implement our approach in the open-source LegUp HLS framework, and provide both sequentially consistent (SC) and weakly consistent ('weak') atomics. Weak atomics necessitate fewer constraints than SC atomics, but suffice for many concurrent algorithms. We confirm, via automatic model-checking, that we correctly implement the semantics defined by the 2011 revision of the C standard. A case study on a circular buffer suggests that circuits synthesised from programs that use atomics can be 2.5x faster than those that use locks, and that weak atomics can yield a further 1.5x speedup

Persistency semantics of the Intel-x86 architecture

Emerging non-volatile memory (NVM) technologies promise the durability of disks with the performance of RAM. To describe the persistency guarantees of NVM, several memory persistency models have been proposed in the literature. However, the persistency semantics of the ubiquitous x86 architecture remains unexplored to date. To close this gap, we develop the Px86 (‘persistent x86’) model, formalising the persistency semantics of Intel-x86 for the first time. We formulate Px86 both operationally and declaratively, and prove that the two characterisations are equivalent. To demonstrate the application of Px86, we develop two persistent libraries over Px86: a persistent transactional library, and a persistent variant of the Michael–Scott queue. Finally, we encode our declarative Px86 model in Alloy and use it to generate persistency litmus tests automatically