Spring 2027

Parallel & Throughput- Optimized Programming

Introduction to high-performance computing focusing on shared-memory systems, multithreaded programming, vectorization, and memory hierarchy using numerical algorithms as case studies.

offering recorded3 credits
Recorded instructors · Spring 2027 Eftychios Sifakis3.6/5

Summary

1 / 6

Past student reviews · Eftychios Sifakis

Lectures primarily involved case study go-throughs with code. Practice exams were provided for review, supporting the course's lenient grading structure.

What you’ll be able to do

  • Evaluate the efficiency and parallelism potential of algorithms, relative to bounds imposed by architectural limitations such as compute and memory bandwidth.

  • Understand the implications of the memory system on parallel scaling, including considerations of memory throughput, latency and prefetching efficiency.

  • Recognize instruction-level parallelism and evaluate how amenable different codes are to compiler-automated (or user-guided, via intrinsics) vectorization.

  • Compare data layouts (dense vs. sparse data, structures-of-arrays vs. arrays-of-structures) to throughput-sensitive codes.

  • Explore computational and memory-related performance implications of fundamental numerical codes such as convolutions, streaming vector operations, and matrix manipulations

Source: UW Guide

Grade history

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average GPA
letter grades
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All recorded terms · compare terms & instructors

Prerequisites

Course map

E C E/​COMP SCI 354 and (MATH 320, 340, 341, 345, or 375), or graduate/professional standing

COMPSCI 557

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027

Eftychios Sifakis

Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 3.59/5 from 27 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

For this course: 5.0/5 raw quality · 3.0/5 difficulty · 1 reviews

RMP profile ↗ · All captured review dates; profile matched by name.

Past student reviews · Eftychios Sifakis

Eftychios Sifakis taught the course with a chill, knowledgeable approach, focusing lectures on case studies and code. Students found the grading structure clear and lenient, noting that he provided practice exams for review. The instructor was described as highly recommendable due to these engaging and supportive teaching methods.

Recent recorded grades — Spring 2025: 3.53 GPA, 79.3% A/AB (n=58 letter grades); Spring 2026: 3.56 GPA, 76.2% A/AB (n=80 letter grades). Includes jointly taught sections.

Historical instructors & teaching patterns

EFTYCHIOS SIFAKIS is recorded teaching in Spring 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 900

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
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Student experience

the class

Past student reviews · Eftychios Sifakis

Eftychios Sifakis taught this course with a chill demeanor, focusing on interesting case studies with code. The grading structure was clear and lenient, making it a highly recommended experience.

Recent recorded grades — Spring 2025: 3.53 GPA, 79.3% A/AB (n=58 letter grades); Spring 2026: 3.56 GPA, 76.2% A/AB (n=80 letter grades).

difficulty & workload

Past student reviews · Eftychios Sifakis

Lectures primarily involved case study go-throughs with code. Practice exams were provided for review, supporting the course's lenient grading structure.

Past student reviews · Eftychios Sifakis

Students found the instructor knowledgeable and the lectures and projects interesting. The overall experience was positive, with the clear grading structure contributing to a favorable outcome.

Topics

  • Shared-memory systems and numerical algorithms
  • GPU computing
  • Performance implications of convolutions, vector operations, and matrix manipulations

Skills

  • Multithreaded programming and vectorization techniques for shared-memory systems.
  • Analyzing memory system implications on parallel scaling, throughput, and latency.
  • Comparing data layouts like dense/sparse and SoA/AoS for throughput optimization.

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2026 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1280 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

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Selected offering source records
COMPSCI 557 · Spring 2027

Parallel & Throughput- Optimized Programming

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:266:026965",
    "course_id": "COMPSCI 557",
    "course_uid": "course_280431e0039b9a9194e1b074",
    "term_id": "1274",
    "source_course_id": "026965",
    "source_subject_id": "266",
    "title": "Parallel & Throughput- Optimized Programming",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Not Applicable"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "263ca2db7f5aa2a651296e38b971329be531e919440806a846892aa2ca6da16e",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}