OpenPOWER Application Optimization

Erlang and XMPP can be used together in several ways: 1. Erlang is well-suited for implementing XMPP servers due to its high concurrency and reliability. ejabberd is an example of a popular Erlang XMPP server. 2. The XMPP protocol can be used to connect Erlang applications and allow them to communicate over the XMPP network. Libraries like Jabberlang facilitate writing Erlang XMPP clients. 3. XMPP provides a flexible messaging backbone that can be extended using Erlang modules. This allows Erlang code to integrate with and enhance standard XMPP server functionality.

This document provides an overview of eBPF/BPF and instructions for creating an eBPF program from scratch. It begins with explaining what eBPF/BPF is, its history and main ideas. It then covers how to build an eBPF program from the Linux kernel source code, including prerequisites, compilation steps, and modifying the makefile. The document also discusses how to program an eBPF program manually, analyzing the eBPF program and loader. It concludes with a promise of a quick demo.

TensorRT is an NVIDIA tool that optimizes and accelerates deep learning models for production deployment. It performs optimizations like layer fusion, reduced precision from FP32 to FP16 and INT8, kernel auto-tuning, and multi-stream execution. These optimizations reduce latency and increase throughput. TensorRT automatically optimizes models by taking in a graph, performing optimizations, and outputting an optimized runtime engine.

The document discusses superscalar processors and provides details about the Pentium 4 architecture as an example of a superscalar CISC machine. It covers topics such as instruction issue policies, register renaming, branch prediction, and the 20 stage pipeline of the Pentium 4. The Pentium 4 decodes x86 instructions into micro-ops, allocates registers and resources out of order, and can dispatch up to 6 micro-ops per cycle to execution units.

Linux kernel provides executable and formalized memory model. These slides describe the nature of parallel programming in the Linux kernel and what memory model is and why it is necessary and important for kernel programmers. The slides were used at KOSSCON 2018 (https://kosscon.kr/).

This document introduces programming models for high-performance computing (HPC). It establishes a taxonomy to classify programming models and systems. The main goals are to introduce the current prominent programming models, including message-passing, shared memory, and bulk synchronous models. The document also discusses that there is no single best solution and that there are trade-offs between different approaches. Implementation stacks and hardware architectures are reviewed to provide context on how programming models map to low-level execution.

This document presents GCMA, a Guaranteed Contiguous Memory Allocator that improves upon the current Contiguous Memory Allocator (CMA) solution in Linux. CMA can have unpredictable latency and even fail when allocating contiguous memory, especially under memory pressure or with background workloads. GCMA guarantees fast latency for contiguous memory allocation, success of allocation, and reasonable memory utilization by using discardable memory as its secondary client instead of movable pages. Experimental results on a Raspberry Pi 2 show that GCMA has significantly faster allocation latency than CMA, keeps camera latency fast even with background workloads, and can improve overall system performance compared to CMA.

Discover, extend, and modernize your current development approach for hetergeneous compute with standards-based OpenFabrics Interfaces* (OFI), message passing interface (MPI), and OpenMP* programming methods on Intel® Xeon Phi™ processors.

VLIW (Very Large Instruction Word) is an architecture that aims to achieve high performance through instruction level parallelism (ILP). It allows multiple independent operations to be specified per instruction. Unlike superscalar architectures, all scheduling is done statically by the compiler in VLIW. The compiler analyzes dependencies, extracts parallelism, and encodes parallel instructions into a single very long instruction word to be executed concurrently by the processor. This reduces hardware complexity compared to dynamic scheduling in superscalar chips.

Load and store instructions first generate an effective address, then perform address translation before accessing the data cache for load or store operations. For loads, the cache is read to return data, while stores write data to the cache. Stores are held in the store buffer until retirement to maintain load-store ordering. Loads can bypass and forward from earlier stores in the store buffer to improve performance. Memory dependencies between loads and stores are difficult to handle due to dynamic addresses and long memory latency. Speculative load disambiguation predicts dependencies to allow out-of-order execution when aliases are rare.

The document discusses the Linux kernel memory model (LKMM). It provides an overview of LKMM, including that it defines ordering rules for the Linux kernel due to weaknesses in the C language standard and need to support multiple hardware architectures. It describes ordering primitives like atomic operations and memory barriers provided by LKMM and how the LKMM was formalized into an executable model that can prove properties of parallel code against the LKMM.

This document provides an introduction to Continuent Tungsten clustering. It discusses key benefits like high availability, multi-site deployment, and ease of use. It examines the clustering architecture including topologies, automatic and manual failover, and rolling maintenance procedures. Commands for monitoring and managing the cluster are also reviewed, including cctrl and tpm diag. A demo shows using cctrl to perform a manual failover by promoting a slave to master.

A microprocessor is an electronic component that is used by a computer to do its work. It is a central processing unit on a single integrated circuit chip containing millions of very small components including transistors, resistors, and diodes that work together.

Faster microprocessor design presentation in American International University-Bangladesh (AIUB). Presentation was taken under the subject "SELECTED TOPICS IN ELECTRICAL AND ELECTRONIC ENGINEERING (PROCESSOR AND DSP HARDWARE DESIGN WITH SYSTEM VERILOG, VHDL AND FPGAS) [MEEE]", as a final semester student of M.Sc at AIUB.

checking dependencies between instructions to determine which instructions can be grouped together for parallel execution; assigning instructions to the functional units on the hardware; determining when instructions are initiated placed together into a single word.

This document discusses superscalar and VLIW architectures. Superscalar processors can execute multiple independent instructions in parallel by checking for dependencies between instructions. VLIW architectures package multiple operations into very long instruction words to execute in parallel on multiple functional units with scheduling done at compile-time rather than run-time. The document compares CISC, RISC, and VLIW instruction sets and outlines advantages and disadvantages of the VLIW approach.

A VLIW processor implements instruction level parallelism by grouping multiple operations into a single very long instruction word. The compiler statically schedules independent instructions to execute in parallel on functional units. This avoids the need for complex hardware to dynamically schedule instructions at runtime. VLIW moves the complexity to the compiler, allowing for simpler hardware that can be lower cost and lower power while achieving higher performance than RISC and CISC chips.

The document discusses strategies for improving application performance on POWER9 processors using IBM XL and open source compilers. It reviews key POWER9 features and outlines common bottlenecks like branches, register spills, and memory issues. It provides guidelines on using compiler options and coding practices to address these bottlenecks, such as unrolling loops, inlining functions, and prefetching data. Tools like perf are also described for analyzing performance bottlenecks.