This Critical Patch Update contains 16 new security patches plus additional third party patches noted below for Oracle Database Products. 1 of these vulnerabilities may be remotely exploitable without authentication, i.e., may be exploited over a network without requiring user credentials. 1 of these patches is applicable to client-only installations, i.e., installations that do not have the Oracle Database Server installed. The English text form of this Risk Matrix can be found here.
This Critical Patch Update contains 2 new security patches plus additional third party patches noted below for Oracle Big Data Graph. Both of these vulnerabilities may be remotely exploitable without authentication, i.e., may be exploited over a network without requiring user credentials. The English text form of this Risk Matrix can be found here.
Microsoft is making available Intel-validated microcode updates that are related to Spectre Variant 3a (CVE-2018-3640 Rogue System Register Read (RSRE)), Spectre Variant 4 (CVE-2018-3639 Speculative Store Bypass (SSB)), and L1TF (CVE-2018-3620 and CVE-2018-3646 L1 Terminal Fault).
The program helps the user to set up compiler, assembler and programmer options. It also has autosearch feature which can do all the work involved. The error results window helps the user to easily identify the errors and correct them. If the user clicks on the compilation error, Microcode studio would jump to line location of the error. It helps the users to view and debug the serial output from their microcontrollers.
In the IBM Z HMC, select Load, select Clear, then enter the loading address (the address of the device containing the /boot/zipl directory with the boot loader). If using a zFCP disk as the boot device, choose Load from SCSI and specify the load address of your FCP adapter plus WWPN and LUN of the boot device. Now start the loading process.
The architecture of Nibbler is shown above. The program counter (PC) provides the address for the program ROM. The program byte is combined with other status bits to provide the address for the microcode ROMs. These ROMs generate the internal control signals needed to load, enable, and increment the other chips in the CPU at the appropriate times. Each ROM outputs a different set of eight of the sixteen total control signals. The program byte is also used to construct addresses for RAM and for jump destinations, as well as to put immediate literal values on the data bus.
The instruction opcode, ALU flags, and phase are combined to form a 7-bit address for the two microcode ROMs, shown at the mid-left. The output of the two ROMs constitutes the 16 control signals needed to orchestrate the behavior of all the other chips. The microcode is stored in two 2Kx8 EEPROMs, so four of the eleven address inputs on each ROM are unused and hard-wired to 0.
The simplest tool is the Microcode Builder, which generates two binary files for the contents of the two microcode ROMs. In the beginning I intended to describe the microcode operations in a text file, using register transfer language, and then the Microcode Builder would assemble it into binary. But when it became clear the microcode would be fairly simple, I dropped the idea of using RTL, and just wrote some C code to generate the required binary data directly. The program contains lots of bit-shifting fun like:
The most complex tool is the machine simulator. Originally developed for BMOW 1, this GUI-based tool simulates the data and control paths of the CPU. It supports source level symbolic debugging, disassembly, microcode debugging, code breakpoints, data breakpoints, memory inspection, and I/O simulation of the LCD and input buttons.
The simulator uses the program binary, the program source and symbol files (if available), and the microcode ROM binaries. In the image above, the simulator is doing source level debugging of a button watcher example program.
You may be interested in the Windows tool to which I link below. I use it to create ROMs and such for my projects. I use it, in particular, to program my microcoded microprocessors and algorithmic state machines: https:
Anurag, here are the parts I used for the Nibbler. Part numbers are from jameco.com.This does not include resistors and capacitors.I used 28C64 instead of 28C16 for the microcode and CY7C199 instead of CY7C168 for RAM.
Some of the more notable items are: highly configurable due to many compile-time configuration options support for many architectures (x86, x86-64, ARM, ARM Thumb, Xtensa) extensive test suite with over 590 tests, and more than 18,500 individual testcases code coverage at 99.2% for the core and at 98.5% for the core plus extended modules fast start-up time from boot to loading of first script (150 microseconds to get to boot.py, on PYBv1.1 running at 168MHz) a simple, fast and robust mark-sweep garbage collector for heap memory a MemoryError exception is raised if the heap is exhausted a RuntimeError exception is raised if the stack limit is reached support for running Python code on a hard interrupt with minimal latency errors have a backtrace and report the line number of the source code constant folding in the parser/compiler pointer tagging to fit small integers, strings and objects in a machine word transparent transition from small integers to big integers support for 64-bit NaN boxing object model support for 30-bit stuffed floats, which don't require heap memory a cross-compiler and frozen bytecode, to have pre-compiled scripts that don't take any RAM (except for any dynamic objects they create) multithreading via the \"_thread\" module, with an optional global-interpreter-lock (still work in progress, only available on selected ports) a native emitter that targets machine code directly rather than the bytecode virtual machine inline assembler (currently Thumb and Xtensa instruction sets only)
3rd Gen Intel Xeon Platinum 8380 CPU: 2x 3rd Gen Intel Xeon Platinum 8380 with 512GB (16 slots/ 32GB/ 3200MHz) total DDR4 memory, microcode 0xd0002b1, HT off, Turbo on, Ubuntu 20.04 LTS, 5.4.0-84-generic kernel, 1x Intel 960GB SSD, Intel Extension for PyTorch v1.8.1, Transformers 4.6.1, MKL 2021.3.0, Bert-large-uncased ( -large-uncased) model, BS=1 per instance, 20 instances/node, 4 cores/instance, test by Intel on 09/17/2021.
Nvidia Ampere A100 GPU: Nvidia Ampere A100 GPU hosted on 2x AMD EPYC 7742 CPU with 1024GB (16 slots/ 64GB/ 3200MHz) total DDR4 memory, microcode 0x8301034, HT off, Turbo on, Ubuntu 20.04 LTS, 5.4.0-80-generic kernel, 1x SAMSUNG 3.5TB SSD, PyTorch 1.8.1, Transformers 4.6.1, CUDA 11.1, Bert-large-uncased ( -large-uncased) model, BS=1 per instance, 7 total instances with MIG enabled, test by Intel on 09/22/2021
3rd Gen Intel Xeon Platinum 8380 CPU: 1-node, 2x 3rd Gen Intel Xeon Platinum 8380 on Coyote Pass with 512 GB (16 slots/ 32GB/ 3200) total DDR4 memory, microcode 0xd0002b1, HT off, Turbo on, Ubuntu 20.04 LTS,5.4.0-84-generic, 1x Intel 960GB SSD OS Drive, Modin 0.10.2, Intel-tensorflow-avx512 2.6.0, oneDNN v2.3 , test by Intel on 09/29/2021
Nvidia Ampere A100 GPU: 1-node, 2x AMD EPYC 7742 on Nvidia DGXA100 920-23687-2530-000 utilizing 1x A100 GPU with 1024 GB (16 slots/ 64GB/ 3200) total DDR4 memory, microcode 0x8301034, HT OFF, Turbo on Ubuntu 20.04 LTS,5.4.0-84-generic , 1x SAMSUNG 3.5TB SSD OS Drive, Modin 0.10.2, tensorflow 2.6.0+nv, CUDA 11.4, test by Intel on 09/29/2021 153554b96e