MGPUSim

Introduction: Decompilers are useful tools for software analysis and support in the absence of source code. They are available for many hardware architectures and programming languages. However, none of the existing decompilers support modern AMD GPU architectures such as AMD GCN and RDNA. Purpose: We aim at developing the first assembly decompiler tool for a modern AMD GPU architecture that generates code in the OpenCL language, which is widely used for programming GPGPUs. Results: We developed the algorithms for the following operations: preprocessing assembly code, searching data accesses, extracting systemvalues, decompiling arithmetic operations and recovering data types. We also developed templates for decompilation of branching operations. Practical relevance: We implemented the presented algorithms in Python as a tool called OpenCLDecompiler, which supports a large subset of AMD GCN instructions. This tool automatically converts disassembled GPGPU code into the equivalent OpenCL code, which reduces the effort required to analyze assembly code.

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“…The AMD GCN architecture [6] is related to OpenCL platform model. A GPU device consists of several compute units.…”

Section: The Amd Gcn Architecturementioning

confidence: 99%

A method for decompilation of AMD GCN kernels to OpenCL

Mihajlenko

Lukin

Stankevich

2021

ICS

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“…We characterize a BFS application from the SHOC Benchmark Suit [11] on a real-world graph and collect the memory trace with a GPU simulator [58]. This BFS application uses CSR graph format and warp-centric execution, similar to Grus.…”

Section: Adaptive Um Policymentioning

confidence: 99%

Grus

Wang

et al. 2021

ACM Trans. Archit. Code Optim.

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Today’s GPU graph processing frameworks face scalability and efficiency issues as the graph size exceeds GPU-dedicated memory limit. Although recent GPUs can over-subscribe memory with Unified Memory (UM), they incur significant overhead when handling graph-structured data. In addition, many popular processing frameworks suffer sub-optimal efficiency due to heavy atomic operations when tracking the active vertices. This article presents Grus, a novel system framework that allows GPU graph processing to stay competitive with the ever-growing graph complexity. Grus improves space efficiency through a UM trimming scheme tailored to the data access behaviors of graph workloads. It also uses a lightweight frontier structure to further reduce atomic operations. With easy-to-use interface that abstracts the above details, Grus shows up to 6.4× average speedup over the state-of-the-art in-memory GPU graph processing framework. It allows one to process large graphs of 5.5 billion edges in seconds with a single GPU.

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Visualization of profiling and tracing in CPU‐GPU programs

Fiorini

Dagenais

2022

Concurrency and Computation

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Summary As the complexity of the toolchain increases for heterogeneous CPU‐GPU systems, the needs for comprehensive tracing and debugging tools also grows. Heterogeneous platforms bring new possibilities but also new performance issues that are hard to detect. Some techniques that were used on CPU programs are now adapted to GPUs. However, there are some concepts specific to GPUs, like SIMD processing, and the effects of the close interactions between the CPUs and the GPUs, with shared virtual memory and user‐level queues. Multiple sources of data need to be extracted and correlated to obtain a more global view of the performance. In this article, we introduce a novel approach for measuring and visualizing performance defects inside CPU‐GPU programs by combining kernel events, compute kernel events, user API calls and memory transfers. We created two new views that combine this information, to help provide a global view. This framework uses the open source user queue system described in the HSA standard. It can easily be adapted to any user queue system for heterogeneous computing devices. We compare this framework with current existing tools and test it against the Rodinia benchmark. We look at how the execution behavior affects the tracing and profiling overhead and we use Trace Compass to visualize the resulting trace.

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MGPUSim

Cited by 52 publications

References 38 publications

A method for decompilation of AMD GCN kernels to OpenCL

A method for decompilation of AMD GCN kernels to OpenCL

Grus

Visualization of profiling and tracing in CPU‐GPU programs

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