MLSysOps on Arshad Siddiqui

ML and DL System Performance

Mon, 01 Jan 0001 00:00:00 +0000

ML and DL System Performance #

Machine learning system optimisation begins with measurement. Before changing an algorithm, adding processors, or moving work to a GPU, we need to understand what is slow, which resource is limiting performance, and how performance changes as the workload grows.

This page covers:

time and space complexity
throughput and latency
the relationship between workload, throughput, and latency
the main measurements used to describe system performance

Learning Objectives #

By the end of this page, you should be able to:

Parallel and Distributed Algorithms

Mon, 01 Jan 0001 00:00:00 +0000

Parallel and Distributed Algorithms #

Parallelisation divides computational work into parts that can execute concurrently. The purpose is to reduce completion time or increase throughput, but the gain depends on how much work is genuinely independent and how much overhead is introduced.

This page covers:

speedup, maximum speedup, and processor efficiency
Amdahl’s Law
data-level parallelism
task-level parallelism
algorithm-specific parallelism
communication, synchronisation, scheduling, and load-balancing overhead
parallel merge sort and matrix multiplication

Learning Objectives #

By the end of this page, you should be able to:

Parallel Programming Models

Mon, 01 Jan 0001 00:00:00 +0000

Parallel Programming Models #

Parallel algorithms need hardware that can execute independent work efficiently. Modern systems therefore combine multiple CPU cores, memory hierarchies, threads, instruction pipelines, GPUs, clusters, and specialised matrix processors.

This page covers:

multi-core CPU organisation
cache and memory hierarchy
processes, threads, scheduling, and synchronisation
instruction pipelining and clock-cycle time
SIMD, MIMD, and SIMT execution
GPGPU architecture and GPU memory behaviour
CPU-only and GPU-accelerated clusters
Tensor Processing Units and systolic arrays

Learning Objectives #

By the end of this page, you should be able to:

ML System Optimisation

Mon, 01 Jan 0001 00:00:00 +0000

ML System Optimisation #

ML System Optimisation studies how to make machine learning workloads faster, more scalable, more memory-efficient, and suitable for different hardware platforms.

The subject connects machine learning algorithms with the systems that train and deploy them: multi-core CPUs, GPUs, distributed clusters, cloud platforms, edge devices, and embedded systems.

ML system optimisation = model quality + computational efficiency + hardware awareness + scalability

The learning path begins with performance measurement and parallel computing, progresses through distributed machine learning and scale-out platforms, and concludes with model compression and resource-constrained deployment.