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GPUTop: a GPU profiling tool

Intel posted info about a new blog post using GPUTop with Caledon (Intel-flavored Android):

We are excited to bring out a new tutorial for profiling gpu on Android. Gputop exposes many GPU parameters module wise such as frequency, busyness, threads, EU activeness etc. These are very helpful in identifying performance bottlenecks as well as impact of performance improvements on the GPU either through graphics software stack or through the graphics application. If you are learning/ new to gpu, this should attract you even more. Please take a look, try out and feel free to share your feedback.

https://01.org/projectceladon/documentation/tutorials/profiling-gpu

https://github.com/rib/gputop

GPU Top is a tool to help developers understand GPU performance counters and provide graphical and machine readable data for the performance analysis of drivers and applications. GPU Top is compatible with all GPU programming apis such as OpenGL, OpenCL or Vulkan since it primarily deals with capturing periodic sampled metrics. GPU Top so far includes a web based interactive UI as well as a non-interactive CSV logging tool suited to being integrated into continuous regression testing systems. Both of these tools can capture metrics from a remote system so as to try an minimize their impact on the system being profiled. GPUs supported so far include: Haswell, Broadwell, Cherryview, Skylake, Broxton, Apollo Lake, Kabylake, Cannonlake and Coffeelake.

https://lists.01.org/mailman/listinfo/celadon

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A Survey of Techniques for Improving Security of GPUs

Graphics processing unit (GPU), although a powerful performance-booster, also has many security vulnerabilities. Due to these, the GPU can act as a safe-haven for stealthy malware and the weakest `link’ in the security `chain’. In this paper, we present a survey of techniques for analyzing and improving GPU security. We classify the works on key attributes to highlight their similarities and differences. More than informing users and researchers about GPU security techniques, this survey aims to increase their awareness about GPU security vulnerabilities and potential countermeasures.

https://arxiv.org/abs/1804.00114

 

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Intel Security Essentials: A Built-in Foundation with Security at the Core

Intel Threat Detection Technology (TDT) announced at RSA. Includes GPU-powered antivirus code.

https://newsroom.intel.com/editorials/securing-digital-world-intel-announces-silicon-level-security-technologies-industry-adoption-rsa-2018/

https://software.intel.com/en-us/blogs/2018/04/16/intel-security-essentials-a-built-in-foundation-with-security-at-the-core

https://www.intel.com/content/www/us/en/security/hardware/hardware-security-overview.html

https://www.engadget.com/2018/04/17/intel-malware-scanner-gpu-processor-cpu-speed/

https://arstechnica.com/gadgets/2018/04/intel-microsoft-to-use-gpu-to-scan-memory-for-malware/

Intel Security Essentials

 

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AMD Vega Pro GPU contains a Security Processor

https://www.heise.de/newsticker/meldung/AMD-Treiber-Radeon-Pro-17-8-fuer-Vega-Hardware-Sicherheit-und-On-the-Fly-Treiberwechsel-3784816.html

http://www.phoronix.com/scan.php?page=news_item&px=Vega-Pro-Secure-Processor

 

 

 

 

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A Study of Overflow Vulnerabilities on GPUs

A Study of Overflow Vulnerabilities on GPUs
Bang Di, Jianhua Sun, Hao Chen

GPU-accelerated computing gains rapidly-growing popularity in many areas such as scientific computing, database systems, and cloud environments. However, there are less investigations on the security implications of concurrently running GPU applications. In this paper, we explore security vulnerabilities of CUDA from multiple dimensions. In particular, we first present a study on GPU stack, and reveal that stack overflow of CUDA can affect the execution of other threads by manipulating different memory spaces. Then, we show that the heap of CUDA is organized in a way that allows threads from the same warp or different blocks or even kernels to overwrite each other’s content, which indicates a high risk of corrupting data or steering the execution flow by overwriting function pointers. Furthermore, we verify that integer overflow and function pointer overflow in struct also can be exploited on GPUs. But other attacks against format string and exception handler seems not feasible due to the design choices of CUDA runtime and programming language features. Finally, we propose potential solutions of preventing the presented vulnerabilities for CUDA.

https://www.aimlab.org/haochen/papers/npc16-overflow.pdf

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